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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Overview\n",
"\n",
"Short term forecasting in the renewable energy sector is becoming increasingly important. With renewables penetrating the market faster than expected, and the inherent uncertainty involved with weather forecasts, it is putting a lot of strain on the existing energy providers and distributors to both manage and balance the power grid. Doing so efficiently requires accurate knowledge of both both supply and demand. As both of these are future events, forecasting is required to predict these factors. Energy demand is a more stable signal than supply of renewable energy which is based on local weather systems relative to the energy generation. Of course demand can also spike unexpectedly with events such as extreme weather spells. Bad predictions can cause energy providers to end up with a shortfall in supply which means they will be required to generate the shortfall by burning more expensive and less climate friendly fuels. It can also result in large oversupply where the company is burning fuel needlessly with is both bad economically and for the climate. Therefore, better predictions in the [1, 48] hour time horizon are absolutely central to efficiently balancing supply and demand in the energy grid. \n",
"\n",
"In this post, we will show you how to implement a short term weather forecast using a type of deep learning known as recurrent neural networks ([RNN](https://en.wikipedia.org/wiki/Recurrent_neural_network)). The particular type of RNN we use is called a Long Short Term Memory ([LSTM](https://en.wikipedia.org/wiki/Long_short-term_memory)) network. We will use [Keras](https://keras.io/) (version 2+) with the [TensorFlow](https://www.tensorflow.org/) backend as the framework for building this network. \n",
"\n",
"In this post we're not going to argue the merits of deep learning via LSTMs vs more classical methods such as [ARIMA](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average) or vice versa. Instead we're simply showing how to approach and implement a forecasting problem using LSTMs. In general simpler machine learning models should be tried first.\n",
"\n",
"\n",
"### Why use a Recurrent Neural Network\n",
"\n",
"Recurrent neural networks enable the learning and encoding of temporal features of a signal. This is ideal for forecasting signals which are in some way predictive based on past events. LSTMs are a type of recurrent networks that overcome some of the historic issues related to training recurrent networks, such as the [vanishing gradients problem](https://en.wikipedia.org/wiki/Vanishing_gradient_problem). We won't delve into the details of LSTMs here and will instead point you to this [article](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) for a thorough overview. \n",
"\n",
"\n",
"### ARIMA\n",
"\n",
"Auto-Regressive Integrated Moving Average ([ARIMA](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average)) models are the most general and commonly used class of models for forecasting of a stationary signal (or a signal which can be made stationary). They support random walk, seasonal trend, non-seasonal exponentional smoothing and autoregressive models. \n",
"\n",
"Lags of the stationarized series in the forecasting equation are called \"autoregressive\" terms, lags of the forecast errors are called \"moving average\" terms, and a time series which needs to be differenced to be made stationary is said to be an \"integrated\" version of a stationary series. Random-walk and random-trend models, autoregressive models, and exponential smoothing models are all special cases of ARIMA models. See [here](https://people.duke.edu/~rnau/411arim.htm) for more details. There are relatively systematic approaches to determining the ARIMA model parameters. There are many many publications and applications of ARIMA to renewables forecasting with various extensions to solve different issues. \n",
"\n",
"To see an application of ARIMA for forecasting and the method for determining the model parameters, see the following [notebook](https://datascience.ibm.com/exchange/public/entry/view/815137c868b916821dec777bdc23013c). \n",
"\n",
"\n",
"### Baseline Comparison\n",
"\n",
"As a baseline model comparison, we will compare the results of the LSTM predictions with that of a persistence method forecast. A persistence method forecast is simply one that uses the observed reading from the previous timestamp to predict the current timestamp. Such a method is very simple but surprisingly effective in complex systems where values between successive timestamps changes little. As the forecast horizon increases, these methods can be seen to be less and less effective. However, cyclical patterns can lead to larger horizons being more accurate than medium horizons (think about the presistence method forecast for the temperature at horizon 12 and 24 hours.. The 24 hour prediction should be more accurate than the 12 hour as it is for the same time of day)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import seaborn as sns\n",
"sns.set_color_codes()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Weather Data\n",
"\n",
"As we want to make predictions on the weather at a given location at a given point in time, we need observational data for that location. Another common approach is to predict the power output of a wind farm which is of course based on the weather and the power conversion properties of the wind turbines. \n",
"\n",
"There are many caveats we won’t go into in this post on the observational data such as the height of the observation tower relative to the turbines and other similarly important factors. An ideal scenario would be observations of the weather data from the same location and height of each turbine in a wind farm. This would provide the most accurate observational data to use in training our models. Sometimes it is easier to model the turbin power output as it can be more stable and incorporate the power conversion properties of the tubines also. However, for the purposes of demonstration, we will use the weather observation available from the nearest NOAA weather station which happens to be El Prat Airport in Barcelona. This is from a height of only 8 metres and measurements at this height tend to be more unstable.\n",
"\n",
"It is also highly advisable to include at least one numerical weather models predictions in your model. These models will help predict the effect of global weather patterns and potential impact that can have on the more local scale. Remember, we are not using the global weather data to build our model and so we cannot accurately attempt to predict global weather systems which can naturally move across local areas.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Observational Data\n",
"\n",
"We get the observational data from NOAA. NOAA collects the weather station observations from stations in countries all around the world. To get it for a given station, you just need to know whats known as the USAF and WBAN IDs. We are using Barcelona El Prat Airport in this notebook but you can change to whatever. \n",
"\n",
"We providea script that allows you to download the data locally\n",
"\n",
" data/download-observations.sh\n",
" \n",
"We then read and parse this data for the exact weather observation station we wish to work with (there are, or have been multiple in El Prat). Don't use data from many stations as its unlikely to be consistent (same height, location etc). \n",
"\n",
"Sometimes the stations have readings every 30 minutes and sometimes every hour. We have seen that not all of the meterological readings are available every 30 minutes and so in this example we only use the hourly data. \n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"import gzip\n",
"from io import BytesIO\n",
"from ish_parser import ish_parser\n",
"\n",
"def read_observations(years, usaf='081810', wban='99999'):\n",
" parser = ish_parser()\n",
" \n",
" for year in years:\n",
" path = \"data/observations/{usaf}-{wban}-{year}.gz\".format(year=year, usaf=usaf, wban=wban)\n",
" with gzip.open(path) as gz:\n",
" parser.loads(bytes.decode(gz.read()))\n",
" \n",
" reports = parser.get_reports()\n",
" \n",
" station_latitudes = [41.283, 41.293] \n",
" observations = pd.DataFrame.from_records(((r.datetime, \n",
" r.air_temperature.get_numeric(),\n",
" (r.precipitation[0]['depth'].get_numeric() if r.precipitation else 0),\n",
" r.humidity.get_numeric(),\n",
" r.sea_level_pressure.get_numeric(),\n",
" r.wind_speed.get_numeric(),\n",
" r.wind_direction.get_numeric()) \n",
" for r in reports if r.latitude in station_latitudes and r.datetime.minute == 0),\n",
" columns=['timestamp', 'AT', 'precipitation', 'humidity', 'pressure', 'wind_speed', 'wind_direction'], \n",
" index='timestamp')\n",
"\n",
" \n",
" return observations\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Numerical Weather Models\n",
"\n",
"The numerical weather model (NWM) we're going to use to help us take into account information other than the local station observations is the NEMS4 model. This is provided free of charge to download by [MeteoBlue](https://www.meteoblue.com) for a given date range for a given station. We read and parse this raw download format here. \n",
"\n",
"Using a weather model is really important as you cannot hope to account for weather systems moving across a local region without it. An important point to note is that the weather model and observations are not meant to match exactly as the model may be predicting for a different height than the observations. However the boundary and ramp events are what we're after. \n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"nems4_lookahead=12\n",
"\n",
"def read_nems4(years, prediction_hours=12):\n",
" predictions=pd.DataFrame()\n",
" for year in years:\n",
" with open('data/NEMS4/{}.json'.format(year)) as json_data:\n",
" d = json.load(json_data)\n",
" if not predictions.empty:\n",
" predictions = predictions.append( pd.DataFrame(d['history_1h']))\n",
" else:\n",
" predictions = pd.DataFrame(d['history_1h'])\n",
"\n",
" predictions = predictions.set_index('time')\n",
" predictions.index.name = 'timestamp'\n",
" \n",
" # shift dataset back 12 hours as its a the value is the prediction for the given timestmap 12 hours previously\n",
" predictions.index = pd.to_datetime(predictions.index) - pd.Timedelta(hours=nems4_lookahead)\n",
" #predictions.index.tz = 'UTC'\n",
" predictions.index = predictions.index.tz_localize('utc')\n",
"\n",
" predictions = predictions[['temperature', 'precipitation', \n",
" 'relativehumidity', 'sealevelpressure', \n",
" 'windspeed']]\n",
" \n",
" predictions = predictions.rename(columns={\n",
" 'windspeed': 'nems4_wind_speed', \n",
" 'temperature': 'nems4_AT',\n",
" 'precipitation': 'nems4_precipitation',\n",
" 'relativehumidity': 'nems4_humidity',\n",
" 'sealevelpressure': 'nems4_pressure'})\n",
" \n",
" return predictions\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Join the NWM and Observations\n",
"\n",
"Next we join the observational and numerical model datasets by their timestamps and print some statistics. Its a good idea to quickly eyeball the statistics here, especially max values. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AT</th>\n",
" <th>precipitation</th>\n",
" <th>humidity</th>\n",
" <th>pressure</th>\n",
" <th>wind_speed</th>\n",
" <th>wind_direction</th>\n",
" <th>nems4_AT</th>\n",
" <th>nems4_precipitation</th>\n",
" <th>nems4_humidity</th>\n",
" <th>nems4_pressure</th>\n",
" <th>nems4_wind_speed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>69319.000000</td>\n",
" <td>69190.000000</td>\n",
" <td>69294.000000</td>\n",
" <td>52921.000000</td>\n",
" <td>69348.000000</td>\n",
" <td>67721.000000</td>\n",
" <td>69364.000000</td>\n",
" <td>69364.000000</td>\n",
" <td>69364.000000</td>\n",
" <td>69364.000000</td>\n",
" <td>69364.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>16.774333</td>\n",
" <td>0.080804</td>\n",
" <td>67.726123</td>\n",
" <td>1016.464798</td>\n",
" <td>4.107509</td>\n",
" <td>236.357408</td>\n",
" <td>16.286344</td>\n",
" <td>0.037997</td>\n",
" <td>71.137665</td>\n",
" <td>1016.023932</td>\n",
" <td>3.486406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>6.758221</td>\n",
" <td>1.097682</td>\n",
" <td>14.477894</td>\n",
" <td>6.858900</td>\n",
" <td>2.106081</td>\n",
" <td>107.675408</td>\n",
" <td>6.709792</td>\n",
" <td>0.285362</td>\n",
" <td>16.547792</td>\n",
" <td>7.103960</td>\n",
" <td>1.969801</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-3.500000</td>\n",
" <td>0.000000</td>\n",
" <td>8.000000</td>\n",
" <td>980.200000</td>\n",
" <td>0.000000</td>\n",
" <td>10.000000</td>\n",
" <td>-5.840000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>982.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>11.600000</td>\n",
" <td>0.000000</td>\n",
" <td>59.000000</td>\n",
" <td>1012.700000</td>\n",
" <td>2.600000</td>\n",
" <td>160.000000</td>\n",
" <td>11.340000</td>\n",
" <td>0.000000</td>\n",
" <td>60.000000</td>\n",
" <td>1012.000000</td>\n",
" <td>2.040000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>16.700000</td>\n",
" <td>0.000000</td>\n",
" <td>69.000000</td>\n",
" <td>1016.700000</td>\n",
" <td>4.100000</td>\n",
" <td>240.000000</td>\n",
" <td>16.470000</td>\n",
" <td>0.000000</td>\n",
" <td>73.000000</td>\n",
" <td>1016.000000</td>\n",
" <td>3.130000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>22.300000</td>\n",
" <td>0.000000</td>\n",
" <td>78.000000</td>\n",
" <td>1020.500000</td>\n",
" <td>5.100000</td>\n",
" <td>340.000000</td>\n",
" <td>21.402500</td>\n",
" <td>0.000000</td>\n",
" <td>85.000000</td>\n",
" <td>1020.000000</td>\n",
" <td>4.590000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>35.300000</td>\n",
" <td>75.900000</td>\n",
" <td>100.000000</td>\n",
" <td>1038.700000</td>\n",
" <td>27.800000</td>\n",
" <td>360.000000</td>\n",
" <td>39.210000</td>\n",
" <td>16.000000</td>\n",
" <td>100.000000</td>\n",
" <td>1039.000000</td>\n",
" <td>22.320000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AT precipitation humidity pressure wind_speed \\\n",
"count 69319.000000 69190.000000 69294.000000 52921.000000 69348.000000 \n",
"mean 16.774333 0.080804 67.726123 1016.464798 4.107509 \n",
"std 6.758221 1.097682 14.477894 6.858900 2.106081 \n",
"min -3.500000 0.000000 8.000000 980.200000 0.000000 \n",
"25% 11.600000 0.000000 59.000000 1012.700000 2.600000 \n",
"50% 16.700000 0.000000 69.000000 1016.700000 4.100000 \n",
"75% 22.300000 0.000000 78.000000 1020.500000 5.100000 \n",
"max 35.300000 75.900000 100.000000 1038.700000 27.800000 \n",
"\n",
" wind_direction nems4_AT nems4_precipitation nems4_humidity \\\n",
"count 67721.000000 69364.000000 69364.000000 69364.000000 \n",
"mean 236.357408 16.286344 0.037997 71.137665 \n",
"std 107.675408 6.709792 0.285362 16.547792 \n",
"min 10.000000 -5.840000 0.000000 1.000000 \n",
"25% 160.000000 11.340000 0.000000 60.000000 \n",
"50% 240.000000 16.470000 0.000000 73.000000 \n",
"75% 340.000000 21.402500 0.000000 85.000000 \n",
"max 360.000000 39.210000 16.000000 100.000000 \n",
"\n",
" nems4_pressure nems4_wind_speed \n",
"count 69364.000000 69364.000000 \n",
"mean 1016.023932 3.486406 \n",
"std 7.103960 1.969801 \n",
"min 982.000000 0.000000 \n",
"25% 1012.000000 2.040000 \n",
"50% 1016.000000 3.130000 \n",
"75% 1020.000000 4.590000 \n",
"max 1039.000000 22.320000 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"years = range(2007, 2015)\n",
"dataset = pd.merge(read_observations(years), read_nems4(years), left_index=True, right_index=True, how='inner')\n",
"\n",
"original = dataset.copy(deep=True)\n",
"dataset.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformations - Preprocessing\n",
"\n",
"There are a number of preprocessing steps we must take on this data. We only perform the bare minimum in this notebook as an example of how to work with the data. Much more time and analysis should be spent working with missing datapoints in particular and investigating or trying to detect where the instruments may not be working well (e.g. it is reading zero for a given period). Looking at the dataframe statistics in the cell above, one can quickly tell if there are outliers which should be filtered - for example, if the wind was 100m/sec at any point it is obviously wrong. \n",
"\n",
"- Dropping Duplicates\n",
"\n",
"As the name implies, we need to ensure we have only unique timepoints. \n",
"\n",
"- Imputing Missing Values\n",
"\n",
"As mentioned, this step needs some more love. We apply very simple forward filling in this notebook. \n",
"\n",
"- Standardize / Stationary Signal\n",
"\n",
"A nice resource for understanding stationarity is [here](https://people.duke.edu/~rnau/411diff.htm). A standardised series is easier to predict. Weather data in particular is non stationary with cyclical trends. Hence, we use first order differencing here to transform the signal from non stationary to stationary. We could spend an entire notebook analysing this and showing why etc. Instead we refer you back to the [resource](https://people.duke.edu/~rnau/411diff.htm). \n",
"\n",
"- Normalize\n",
"\n",
"First order differencing results in an approximately zero mean. Despite some sources, it is better to have the data centered around zero for a neural network than have it scaled between [0,1] for example (especially when using tanh activation function). However, an important reason for apply some scaling is to ensure that certain input features don't dominate the learning (if one feature after standardising had range [-1000,1000] and another [-1,1], then the contribution of the first to the distance will dominate that of the second). See this [article](http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-16.html) for an interesting discussion on the topic. \n",
"\n",
"Be careful not to use the MinMaxScaler after standardising the signal via first order differences. If you do, you may notice that the mean is no longer zero. This is exactly what we don't want. Therefore in this notebook we use the sklearn StandardScaler which ensures centering and scaling are applied on each input column (feature). \n",
"\n",
"- Add Horizon (Prediction) columns\n",
"\n",
"As we are working with first order difference values for the wind speed, our predictions are going to be the differences also at the various time horizons. There are two ways we can achieve this. \n",
"\n",
"The first way is to predict the difference for each time point up to our max horizon. That is if we have a 3 hour horizon we would predict 3 values for hour [1,2,3] respectively. Then to get the prediction at hour 3 we would get the current real value and add the differences for each hour. A major issue with this approach is that the errors compound. At hour 3 we have included the errors for the predictions at hour 1 and 2 also. Extend that to a larger horizon and it doesn't work well. Additionally, you must predict the value at each time step up to the horizon and if the horizon is large it increases the complexity of the network (number of parameters) needlessly.\n",
"\n",
"The second approach is to simply predict the difference at the horizons. So if we wanted to predict for horizons [1,3] then we only need to predict these two values and the prediction at hour 3 can be obtained by adding the 3 hour difference to the current value. This prevents compounding of errors. However, we are not sure if this approach causes stationarity problems in our predictions. "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import preprocessing\n",
"\n",
"pd.options.mode.chained_assignment = None\n",
"np.random.seed(1234)\n",
"\n",
"def drop_duplicates(df):\n",
" print(\"Number of duplicates: {}\".format(len(df.index.get_duplicates())))\n",
" return df[~df.index.duplicated(keep='first')]\n",
" \n",
"def impute_missing(df):\n",
" # todo test with moving average / mean or something smarter than forward fill\n",
" print(\"Number of rows with nan: {}\".format(np.count_nonzero(df.isnull())))\n",
" df.fillna(method='ffill', inplace=True)\n",
" return df\n",
" \n",
"def first_order_difference(data, columns):\n",
" for column in columns:\n",
" data[column+'_d'] = data[column].diff(periods=1)\n",
" \n",
" return data.dropna()\n",
"\n",
"def derive_prediction_columns(data, column, horizons):\n",
" for look_ahead in horizons:\n",
" data['prediction_' + str(look_ahead)] = data[column].diff(periods=look_ahead).shift(-look_ahead)\n",
" \n",
" return data.dropna()\n",
"\n",
"def scale_features(scaler, features):\n",
" scaler.fit(features)\n",
" \n",
" scaled = scaler.transform(features)\n",
" scaled = pd.DataFrame(scaled, columns=features.columns)\n",
" \n",
" return scaled\n",
"\n",
"def inverse_prediction_scale(scaler, predictions, original_columns, column):\n",
" loc = original_columns.get_loc(column)\n",
" \n",
" inverted = np.zeros((len(predictions), len(original_columns)))\n",
" inverted[:,loc] = np.reshape(predictions, (predictions.shape[0],))\n",
" \n",
" inverted = scaler.inverse_transform(inverted)[:,loc]\n",
" \n",
" return inverted\n",
"\n",
"def invert_all_prediction_scaled(scaler, predictions, original_columns, horizons):\n",
" inverted = np.zeros(predictions.shape)\n",
" \n",
" for col_idx, horizon in enumerate(horizons):\n",
" inverted[:,col_idx] = inverse_prediction_scale(\n",
" scaler, predictions[:,col_idx], \n",
" original_columns,\n",
" \"prediction_\" + str(horizon))\n",
" \n",
" return inverted\n",
"\n",
"def inverse_prediction_difference(predictions, original):\n",
" return predictions + original\n",
"\n",
"def invert_all_prediction_differences(predictions, original):\n",
" inverted = predictions\n",
" \n",
" for col_idx, horizon in enumerate(horizons):\n",
" inverted[:, col_idx] = inverse_prediction_difference(predictions[:,col_idx], original)\n",
" \n",
" return inverted"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/prl900/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: FutureWarning: 'get_duplicates' is deprecated and will be removed in a future release. You can use idx[idx.duplicated()].unique() instead\n",
" import sys\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of duplicates: 165\n",
"Number of rows with nan: 18335\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>wind_speed</th>\n",
" <th>nems4_wind_speed</th>\n",
" <th>AT</th>\n",
" <th>nems4_AT</th>\n",
" <th>humidity</th>\n",
" <th>nems4_humidity</th>\n",
" <th>pressure</th>\n",
" <th>nems4_pressure</th>\n",
" <th>wind_speed_d</th>\n",
" <th>nems4_wind_speed_d</th>\n",
" <th>AT_d</th>\n",
" <th>nems4_AT_d</th>\n",
" <th>humidity_d</th>\n",
" <th>nems4_humidity_d</th>\n",
" <th>pressure_d</th>\n",
" <th>nems4_pressure_d</th>\n",
" <th>prediction_1</th>\n",
" <th>prediction_6</th>\n",
" <th>prediction_12</th>\n",
" <th>prediction_24</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" <td>6.917400e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-1.774970e-16</td>\n",
" <td>-2.958284e-17</td>\n",
" <td>-8.874851e-17</td>\n",
" <td>1.512012e-16</td>\n",
" <td>-2.728195e-16</td>\n",
" <td>2.169408e-16</td>\n",
" <td>-6.533698e-15</td>\n",
" <td>6.058729e-15</td>\n",
" <td>1.687467e-17</td>\n",
" <td>-6.997676e-18</td>\n",
" <td>-1.432919e-17</td>\n",
" <td>-7.703863e-19</td>\n",
" <td>4.474661e-18</td>\n",
" <td>1.882311e-17</td>\n",
" <td>5.135909e-18</td>\n",
" <td>-2.870973e-17</td>\n",
" <td>-1.406918e-17</td>\n",
" <td>-8.853023e-18</td>\n",
" <td>2.018412e-17</td>\n",
" <td>1.943942e-17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" <td>1.000007e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-1.949640e+00</td>\n",
" <td>-1.772246e+00</td>\n",
" <td>-3.006126e+00</td>\n",
" <td>-3.301981e+00</td>\n",
" <td>-4.123350e+00</td>\n",
" <td>-4.237233e+00</td>\n",
" <td>-5.141749e+00</td>\n",
" <td>-4.792282e+00</td>\n",
" <td>-1.447029e+01</td>\n",
" <td>-1.505706e+01</td>\n",
" <td>-7.907768e+00</td>\n",
" <td>-8.718267e+00</td>\n",
" <td>-7.111558e+00</td>\n",
" <td>-1.234208e+01</td>\n",
" <td>-2.516970e+01</td>\n",
" <td>-7.002624e+00</td>\n",
" <td>-1.447017e+01</td>\n",
" <td>-8.398437e+00</td>\n",
" <td>-7.189374e+00</td>\n",
" <td>-1.002421e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-7.154224e-01</td>\n",
" <td>-7.350207e-01</td>\n",
" <td>-7.549174e-01</td>\n",
" <td>-7.358887e-01</td>\n",
" <td>-6.021523e-01</td>\n",
" <td>-6.725854e-01</td>\n",
" <td>-5.349560e-01</td>\n",
" <td>-5.659004e-01</td>\n",
" <td>-7.199503e-01</td>\n",
" <td>-5.175180e-01</td>\n",
" <td>-5.715896e-01</td>\n",
" <td>-5.316924e-01</td>\n",
" <td>-5.202952e-01</td>\n",
" <td>-5.289195e-01</td>\n",
" <td>-3.594237e-01</td>\n",
" <td>4.049305e-05</td>\n",
" <td>-7.199555e-01</td>\n",
" <td>-6.081040e-01</td>\n",
" <td>-6.658500e-01</td>\n",
" <td>-5.969130e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-3.373553e-03</td>\n",
" <td>-1.808171e-01</td>\n",
" <td>-1.438836e-02</td>\n",
" <td>2.901856e-02</td>\n",
" <td>8.827862e-02</td>\n",
" <td>1.128455e-01</td>\n",
" <td>6.592996e-02</td>\n",
" <td>-2.382816e-03</td>\n",
" <td>-3.746619e-05</td>\n",
" <td>-5.163472e-05</td>\n",
" <td>-9.521444e-02</td>\n",
" <td>-1.568013e-01</td>\n",
" <td>6.770269e-05</td>\n",
" <td>2.803762e-05</td>\n",
" <td>1.455451e-04</td>\n",
" <td>4.049305e-05</td>\n",
" <td>-4.891377e-05</td>\n",
" <td>-7.800952e-05</td>\n",
" <td>-1.842817e-04</td>\n",
" <td>-2.484162e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>4.713257e-01</td>\n",
" <td>5.615107e-01</td>\n",
" <td>8.298147e-01</td>\n",
" <td>7.626139e-01</td>\n",
" <td>7.096665e-01</td>\n",
" <td>8.378586e-01</td>\n",
" <td>5.952819e-01</td>\n",
" <td>5.611348e-01</td>\n",
" <td>4.319102e-01</td>\n",
" <td>4.543091e-01</td>\n",
" <td>3.811608e-01</td>\n",
" <td>3.750209e-01</td>\n",
" <td>5.204306e-01</td>\n",
" <td>5.289755e-01</td>\n",
" <td>3.597148e-01</td>\n",
" <td>4.049305e-05</td>\n",
" <td>4.318950e-01</td>\n",
" <td>6.079480e-01</td>\n",
" <td>6.654815e-01</td>\n",
" <td>5.964162e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.124700e+01</td>\n",
" <td>9.576217e+00</td>\n",
" <td>2.740380e+00</td>\n",
" <td>3.415187e+00</td>\n",
" <td>2.228615e+00</td>\n",
" <td>1.744125e+00</td>\n",
" <td>3.227735e+00</td>\n",
" <td>3.237843e+00</td>\n",
" <td>1.929363e+01</td>\n",
" <td>1.312593e+01</td>\n",
" <td>7.812614e+00</td>\n",
" <td>8.657498e+00</td>\n",
" <td>7.632057e+00</td>\n",
" <td>1.146056e+01</td>\n",
" <td>2.984440e+01</td>\n",
" <td>8.403238e+00</td>\n",
" <td>1.929345e+01</td>\n",
" <td>9.576331e+00</td>\n",
" <td>8.220788e+00</td>\n",
" <td>7.796169e+00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" wind_speed nems4_wind_speed AT nems4_AT \\\n",
"count 6.917400e+04 6.917400e+04 6.917400e+04 6.917400e+04 \n",
"mean -1.774970e-16 -2.958284e-17 -8.874851e-17 1.512012e-16 \n",
"std 1.000007e+00 1.000007e+00 1.000007e+00 1.000007e+00 \n",
"min -1.949640e+00 -1.772246e+00 -3.006126e+00 -3.301981e+00 \n",
"25% -7.154224e-01 -7.350207e-01 -7.549174e-01 -7.358887e-01 \n",
"50% -3.373553e-03 -1.808171e-01 -1.438836e-02 2.901856e-02 \n",
"75% 4.713257e-01 5.615107e-01 8.298147e-01 7.626139e-01 \n",
"max 1.124700e+01 9.576217e+00 2.740380e+00 3.415187e+00 \n",
"\n",
" humidity nems4_humidity pressure nems4_pressure \\\n",
"count 6.917400e+04 6.917400e+04 6.917400e+04 6.917400e+04 \n",
"mean -2.728195e-16 2.169408e-16 -6.533698e-15 6.058729e-15 \n",
"std 1.000007e+00 1.000007e+00 1.000007e+00 1.000007e+00 \n",
"min -4.123350e+00 -4.237233e+00 -5.141749e+00 -4.792282e+00 \n",
"25% -6.021523e-01 -6.725854e-01 -5.349560e-01 -5.659004e-01 \n",
"50% 8.827862e-02 1.128455e-01 6.592996e-02 -2.382816e-03 \n",
"75% 7.096665e-01 8.378586e-01 5.952819e-01 5.611348e-01 \n",
"max 2.228615e+00 1.744125e+00 3.227735e+00 3.237843e+00 \n",
"\n",
" wind_speed_d nems4_wind_speed_d AT_d nems4_AT_d \\\n",
"count 6.917400e+04 6.917400e+04 6.917400e+04 6.917400e+04 \n",
"mean 1.687467e-17 -6.997676e-18 -1.432919e-17 -7.703863e-19 \n",
"std 1.000007e+00 1.000007e+00 1.000007e+00 1.000007e+00 \n",
"min -1.447029e+01 -1.505706e+01 -7.907768e+00 -8.718267e+00 \n",
"25% -7.199503e-01 -5.175180e-01 -5.715896e-01 -5.316924e-01 \n",
"50% -3.746619e-05 -5.163472e-05 -9.521444e-02 -1.568013e-01 \n",
"75% 4.319102e-01 4.543091e-01 3.811608e-01 3.750209e-01 \n",
"max 1.929363e+01 1.312593e+01 7.812614e+00 8.657498e+00 \n",
"\n",
" humidity_d nems4_humidity_d pressure_d nems4_pressure_d \\\n",
"count 6.917400e+04 6.917400e+04 6.917400e+04 6.917400e+04 \n",
"mean 4.474661e-18 1.882311e-17 5.135909e-18 -2.870973e-17 \n",
"std 1.000007e+00 1.000007e+00 1.000007e+00 1.000007e+00 \n",
"min -7.111558e+00 -1.234208e+01 -2.516970e+01 -7.002624e+00 \n",
"25% -5.202952e-01 -5.289195e-01 -3.594237e-01 4.049305e-05 \n",
"50% 6.770269e-05 2.803762e-05 1.455451e-04 4.049305e-05 \n",
"75% 5.204306e-01 5.289755e-01 3.597148e-01 4.049305e-05 \n",
"max 7.632057e+00 1.146056e+01 2.984440e+01 8.403238e+00 \n",
"\n",
" prediction_1 prediction_6 prediction_12 prediction_24 \n",
"count 6.917400e+04 6.917400e+04 6.917400e+04 6.917400e+04 \n",
"mean -1.406918e-17 -8.853023e-18 2.018412e-17 1.943942e-17 \n",
"std 1.000007e+00 1.000007e+00 1.000007e+00 1.000007e+00 \n",
"min -1.447017e+01 -8.398437e+00 -7.189374e+00 -1.002421e+01 \n",
"25% -7.199555e-01 -6.081040e-01 -6.658500e-01 -5.969130e-01 \n",
"50% -4.891377e-05 -7.800952e-05 -1.842817e-04 -2.484162e-04 \n",
"75% 4.318950e-01 6.079480e-01 6.654815e-01 5.964162e-01 \n",
"max 1.929345e+01 9.576331e+00 8.220788e+00 7.796169e+00 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset = drop_duplicates(dataset)\n",
"dataset = impute_missing(dataset)\n",
"\n",
"#select features we're going to use\n",
"features = dataset[['wind_speed', \n",
" 'nems4_wind_speed', \n",
" 'AT', \n",
" 'nems4_AT', \n",
" 'humidity', \n",
" 'nems4_humidity',\n",
" 'pressure',\n",
" 'nems4_pressure']]\n",
"\n",
"# the time horizons we're going to predict (in hours)\n",
"horizons = [1, 6, 12, 24]\n",
"\n",
"features = first_order_difference(features, features.columns)\n",
"features = derive_prediction_columns(features, 'wind_speed', horizons)\n",
"\n",
"scaler = preprocessing.StandardScaler()\n",
"scaled = scale_features(scaler, features)\n",
"\n",
"scaled.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating the Test & Train Datasets for Keras LSTM\n",
"\n",
"In Keras there are two types of LSTM configurations. One is called stateful and the other non stateful. This terminology can be confusing as after all, the very reason for using LSTMs is their temporal memory (i.e. state). This is different to what the term stateful in the LSTM configuration in Keras means.\n",
"\n",
"A stateful Keras LSTM network is one where the internal LSTM units are not reset at all during a training epoc (in fact even between epocs one must manually reset them). This means that the LSTMs build and keep state for the entire training set which means the data must be played through in order. While this is desirable to learn longer term temporal features and dependencies it can be problematic if we have a certain temporal window we want to focus on. \n",
"\n",
"A non stateful LSTM in Keras terminology resembles more of a classic sliding window approach to training, except where the successive timesteps in the window are framed one after another as a two dimensional array instead of flattened out into a long one dimensional array. This allows the LSTM to learn the state and temporal dependencies between the frames in the time window and Keras will automatically reset the state between each training window. This means that there is no ordering or continuity requirements on the training data outside of the windows.\n",
"\n",
"We cannot reasonably expect the model to pick up very long term dependencies for the weather here given even the most accurate global weather models are chance at horizons of 10 days. Therefore we define our window size (sequence_length) to be 48 hours. You can change to 72 etc to see the difference. The nice thing with LSTMs is that you do not increase the number of parameters in the LSTM network by changing this sequence_lenght.\n",
"\n",
"The shape of the X datasets will be a 3D tensor\n",
"\n",
" (n_samples, sequence_length, n_features)\n",
"\n",
"\n",
"We are predicting multiple time horizons into the future. Hence we will have multiple network outputs, one for each horizon, which is a 2D tensor\n",
"\n",
" (n_samples, n_horizons)\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def prepare_test_train(data, features, predictions, sequence_length, split_percent=0.9):\n",
" \n",
" num_features = len(features)\n",
" num_predictions = len(predictions)\n",
" \n",
" # make sure prediction cols are at end\n",
" columns = features + predictions\n",
" \n",
" data = data[columns].values\n",
" \n",
" print(\"Using {} features to predict {} horizons\".format(num_features, num_predictions))\n",
" \n",
" result = []\n",
" for index in range(len(data) - sequence_length+1):\n",
" result.append(data[index:index + sequence_length])\n",
"\n",
" result = np.array(result)\n",
" # shape (n_samples, sequence_length, num_features + num_predictions)\n",
" print(\"Shape of data: {}\".format(np.shape(result)))\n",
" \n",
" row = round(split_percent * result.shape[0])\n",
" train = result[:row, :]\n",
" \n",
" X_train = train[:, :, :-num_predictions]\n",
" y_train = train[:, -1, -num_predictions:]\n",
" X_test = result[row:, :, :-num_predictions]\n",
" y_test = result[row:, -1, -num_predictions:]\n",
" \n",
" print(\"Shape of X train: {}\".format(np.shape(X_train)))\n",
" print(\"Shape of y train: {}\".format(np.shape(y_train)))\n",
" print(\"Shape of X test: {}\".format(np.shape(X_test)))\n",
" \n",
" X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], num_features))\n",
" X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], num_features))\n",
" \n",
" y_train = np.reshape(y_train, (y_train.shape[0], num_predictions))\n",
" y_test = np.reshape(y_test, (y_test.shape[0], num_predictions))\n",
" \n",
" return X_train, y_train, X_test, y_test, row"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using 8 features to predict 4 horizons\n",
"Shape of data: (69127, 48, 12)\n",
"Shape of X train: (62214, 48, 8)\n",
"Shape of y train: (62214, 4)\n",
"Shape of X test: (6913, 48, 8)\n"
]
}
],
"source": [
"sequence_length = 48\n",
"\n",
"prediction_cols = ['prediction_' + str(h) for h in horizons]\n",
"feature_cols = ['wind_speed_d', 'nems4_wind_speed_d', \n",
" 'AT_d', 'nems4_AT_d', \n",
" 'humidity_d', 'nems4_humidity_d', \n",
" 'pressure_d', 'nems4_pressure_d']\n",
"\n",
"X_train, y_train, X_test, y_test, row_split = prepare_test_train(\n",
" scaled,\n",
" feature_cols,\n",
" prediction_cols,\n",
" sequence_length,\n",
" split_percent = 0.9)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Validate Test & Train Dataset Preparation\n",
"\n",
"Ensure we can undo each transformation to get back the original signal. Sanity checks are always good\n",
"\n",
"This is surprisingly tricky when performing first order differencing and the data preprocesssing / structuring required by the LSTM. Ensuring you are adding the correct \"original\" value is really important. \n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
"\n",
"#(-1 is because we only take the last y row in each sequence)\n",
"sequence_offset = sequence_length - 1\n",
"\n",
"# validate train\n",
"inverse_scale = invert_all_prediction_scaled(scaler, y_train, scaled.columns, horizons)\n",
"\n",
"assert(mean_squared_error(\n",
" features[prediction_cols][sequence_offset:row_split+sequence_offset], \n",
" inverse_scale) < 1e-10)\n",
"\n",
"\n",
"undiff_prediction = invert_all_prediction_differences(\n",
" inverse_scale, \n",
" features['wind_speed'][sequence_offset:row_split+sequence_offset])\n",
"\n",
"for i, horizon in enumerate(horizons):\n",
" assert(mean_squared_error(\n",
" features['wind_speed'][sequence_offset+horizon:row_split+sequence_offset+horizon], \n",
" undiff_prediction[:,i]) < 1e-10)\n",
"\n",
" \n",
"# validate test\n",
"inverse_scale = invert_all_prediction_scaled(scaler, y_test, scaled.columns, horizons)\n",
"\n",
"assert(mean_squared_error(\n",
" features[prediction_cols][sequence_offset+row_split:], \n",
" inverse_scale) < 1e-10)\n",
"\n",
"undiff_prediction = invert_all_prediction_differences(\n",
" inverse_scale, \n",
" features['wind_speed'][sequence_offset+row_split:])\n",
"\n",
"for i, horizon in enumerate(horizons):\n",
" assert(mean_squared_error(\n",
" features['wind_speed'][sequence_offset+row_split+horizon:], \n",
" undiff_prediction[:-horizon,i]) < 1e-10)\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build the LSTM Model\n",
"\n",
"Build the non stateful LSTM network. \n",
"\n",
"We apply regularisation via dropout between each layer in the network. This should help overfitting but we could also add L2 weight regularization. The RMSProp optimizer is recommended when working with LSTMs. The only tunable property of this is the learning rate. Keras has some callbacks that allow for tuning of this as the training progresses (e.g. see [ReduceLROnPlateau](https://keras.io/callbacks/#reducelronplateau)). \n",
"\n",
"The first and last layers deserve a comment. The input_shape argument in the first layer specifies the number of input features which is `X_train.shape[2]`. The last layer is the output layer (hence linear activation) and the size is equal to the number of time horizons we're predicting - `y_train.shape[1]`. \n",
"\n",
"We're using a very simple network structure in this notebook with a small number of parameters in each layer. This means we should expect to see trends as opposed to capturing the finer details of the signal. With better and more abundant training data, we could look to use a bigger network with more regularization if necessary to capture higher level and more complex latent features of the signal. "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Activation, Dropout\n",
"from tensorflow.keras.layers import LSTM\n",
"from tensorflow.keras.optimizers import RMSprop\n",
"\n",
"def build_model(layers):\n",
" model = Sequential()\n",
" \n",
" model.add(LSTM(layers[1], input_shape=(None, layers[0]), return_sequences=True))\n",
" model.add(Dropout(0.2))\n",
" \n",
" model.add(LSTM(layers[2], return_sequences=True))\n",
" model.add(Dropout(0.2))\n",
" \n",
" model.add(LSTM(layers[3], return_sequences=False))\n",
" model.add(Dropout(0.2))\n",
" \n",
" model.add(Dense(layers[4], activation=\"linear\"))\n",
" \n",
" model.compile(loss=\"mse\", optimizer='rmsprop')\n",
" \n",
" print(model.summary())\n",
" \n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train and Evaluate the Model\n",
"\n",
"Fit the model with the training data. We use a validation split of the training data (10%) to allow for things such as early stopping to work (it validates the validation loss against the training loss). \n",
"\n",
"The size of each network layer is passed in via an argument. The first and last values in that layers array represent the input and output size respectively. The other 3 values represent the size of the 3 layers of the network. \n",
"\n",
"The batch_size here refers to the number of samples to be taken between gradient updates. Powers of two are recommended and typically values are 256, 512 etc. Batch learning greatly improves the speed of training and the stability of the convergence. \n",
"\n",
"\n",
"#### Using AWS or Google Cloud GPUs\n",
"\n",
"While a GPU is absolutely essential to train a large network, for a small network like we are playing with in this notebook you can train the model on a good i7 cpu. I found that it was about 8 times faster on a p2.xlarge GPU instance on AWS compared to locally on my 4 core i7. Most of the cloud providers give access to a variety of options for deep learning with optimized instances and GPUs. We won't go into the details of how to do for each or the benefits of one versus the other in this post. We trained the model on a p2.2xlarge instance on AWS using the AWS Spot market which cost approx $0.3 / hour. Training 50 epocs took approximately 20 minutes. \n",
"\n",
"\n",
"#### Hyperparameter Tuning\n",
"\n",
"Hyperparameter tuning is an essential part of any machine learning process. Do not fall into the trap of trying to manually hand tune the parameters. There are many formal approaches which optimize this (see my blog [post](http://www.willfleury.com/machine-learning/bayesian-optimization/2017/05/15/hyperparameter-optimisation.html) on the suject). If you are running on Google Cloud Machine Learning it actually has the ability to perform the hyperparameter optimization built into the API. Exactly what type of optimization it performs under the hood is unclear however. \n",
"\n",
"*As mentioned already in this post, we have not performed an exhaustive training on this model and as such one could easily achieve better results by changing the number of layers, the size of the layers, the sequence_lenght or any one of the other tunable hyperparameters for this model.*\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.callbacks import TensorBoard, EarlyStopping, ReduceLROnPlateau\n",
"\n",
"def run_network(X_train, y_train, X_test, layers, epochs, batch_size=512):\n",
" model = build_model(layers)\n",
" history = None\n",
" \n",
" try:\n",
" history = model.fit(\n",
" X_train, y_train, \n",
" batch_size=batch_size, \n",
" epochs=epochs, \n",
" validation_split=0.1,\n",
" callbacks=[\n",
" TensorBoard(log_dir='/tmp/tensorboard', write_graph=True),\n",
" #EarlyStopping(monitor='val_loss', patience=5, mode='auto')\n",
" ])\n",
" except KeyboardInterrupt:\n",
" print(\"\\nTraining interrupted\")\n",
" \n",
" predicted = model.predict(X_test)\n",
" \n",
" return model, predicted, history\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"lstm (LSTM) (None, None, 20) 2320 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, None, 20) 0 \n",
"_________________________________________________________________\n",
"lstm_1 (LSTM) (None, None, 15) 2160 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, None, 15) 0 \n",
"_________________________________________________________________\n",
"lstm_2 (LSTM) (None, 20) 2880 \n",
"_________________________________________________________________\n",
"dropout_2 (Dropout) (None, 20) 0 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 4) 84 \n",
"=================================================================\n",
"Total params: 7,444\n",
"Trainable params: 7,444\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n",
"Train on 55992 samples, validate on 6222 samples\n",
"Epoch 1/50\n",
" 512/55992 [..............................] - ETA: 6:46 - loss: 1.0002WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.480647). Check your callbacks.\n",
"55992/55992 [==============================] - 16s 284us/sample - loss: 0.8780 - val_loss: 0.8084\n",
"Epoch 2/50\n",
"55992/55992 [==============================] - 10s 184us/sample - loss: 0.7723 - val_loss: 0.7308\n",
"Epoch 3/50\n",
"55992/55992 [==============================] - 10s 185us/sample - loss: 0.7173 - val_loss: 0.6648\n",
"Epoch 4/50\n",
"55992/55992 [==============================] - 10s 187us/sample - loss: 0.6698 - val_loss: 0.6251\n",
"Epoch 5/50\n",
"55992/55992 [==============================] - 11s 189us/sample - loss: 0.6460 - val_loss: 0.6132\n",
"Epoch 6/50\n",
"55992/55992 [==============================] - 11s 189us/sample - loss: 0.6288 - val_loss: 0.5989\n",
"Epoch 7/50\n",
"55992/55992 [==============================] - 11s 189us/sample - loss: 0.6199 - val_loss: 0.5873\n",
"Epoch 8/50\n",
"55992/55992 [==============================] - 10s 187us/sample - loss: 0.6112 - val_loss: 0.5841\n",
"Epoch 9/50\n",
"55992/55992 [==============================] - 11s 192us/sample - loss: 0.6048 - val_loss: 0.5814\n",
"Epoch 10/50\n",
"55992/55992 [==============================] - 10s 187us/sample - loss: 0.6003 - val_loss: 0.5799\n",
"Epoch 11/50\n",
"55992/55992 [==============================] - 11s 189us/sample - loss: 0.5949 - val_loss: 0.5734\n",
"Epoch 12/50\n",
"55992/55992 [==============================] - 12s 208us/sample - loss: 0.5899 - val_loss: 0.5797\n",
"Epoch 13/50\n",
"55992/55992 [==============================] - 11s 198us/sample - loss: 0.5871 - val_loss: 0.5851\n",
"Epoch 14/50\n",
"55992/55992 [==============================] - 11s 195us/sample - loss: 0.5854 - val_loss: 0.5876\n",
"Epoch 15/50\n",
"55992/55992 [==============================] - 11s 194us/sample - loss: 0.5823 - val_loss: 0.5640\n",
"Epoch 16/50\n",
"55992/55992 [==============================] - 11s 203us/sample - loss: 0.5794 - val_loss: 0.5672\n",
"Epoch 17/50\n",
"55992/55992 [==============================] - 11s 194us/sample - loss: 0.5778 - val_loss: 0.5653\n",
"Epoch 18/50\n",
"55992/55992 [==============================] - 12s 216us/sample - loss: 0.5754 - val_loss: 0.5766\n",
"Epoch 19/50\n",
"55992/55992 [==============================] - 11s 205us/sample - loss: 0.5745 - val_loss: 0.5669\n",
"Epoch 20/50\n",
"55992/55992 [==============================] - 11s 199us/sample - loss: 0.5720 - val_loss: 0.5634\n",
"Epoch 21/50\n",
"55992/55992 [==============================] - 15s 266us/sample - loss: 0.5715 - val_loss: 0.5627\n",
"Epoch 22/50\n",
"55992/55992 [==============================] - 11s 198us/sample - loss: 0.5682 - val_loss: 0.5636\n",
"Epoch 23/50\n",
"55992/55992 [==============================] - 11s 197us/sample - loss: 0.5675 - val_loss: 0.5658\n",
"Epoch 24/50\n",
"55992/55992 [==============================] - 11s 200us/sample - loss: 0.5667 - val_loss: 0.5639\n",
"Epoch 25/50\n",
"55992/55992 [==============================] - 11s 194us/sample - loss: 0.5644 - val_loss: 0.5755\n",
"Epoch 26/50\n",
"55992/55992 [==============================] - 11s 203us/sample - loss: 0.5634 - val_loss: 0.5621\n",
"Epoch 27/50\n",
"55992/55992 [==============================] - 13s 226us/sample - loss: 0.5623 - val_loss: 0.5654\n",
"Epoch 28/50\n",
"55992/55992 [==============================] - 14s 258us/sample - loss: 0.5621 - val_loss: 0.5673\n",
"Epoch 29/50\n",
"55992/55992 [==============================] - 12s 218us/sample - loss: 0.5607 - val_loss: 0.5646\n",
"Epoch 30/50\n",
"55992/55992 [==============================] - 14s 255us/sample - loss: 0.5599 - val_loss: 0.5653\n",
"Epoch 31/50\n",
"55992/55992 [==============================] - 13s 240us/sample - loss: 0.5573 - val_loss: 0.5620\n",
"Epoch 32/50\n",
"55992/55992 [==============================] - 12s 207us/sample - loss: 0.5567 - val_loss: 0.5685\n",
"Epoch 33/50\n",
"55992/55992 [==============================] - 12s 222us/sample - loss: 0.5571 - val_loss: 0.5658\n",
"Epoch 34/50\n",
"55992/55992 [==============================] - 12s 214us/sample - loss: 0.5545 - val_loss: 0.5640\n",
"Epoch 35/50\n",
"55992/55992 [==============================] - 11s 204us/sample - loss: 0.5555 - val_loss: 0.5768\n",
"Epoch 36/50\n",
"55992/55992 [==============================] - 12s 216us/sample - loss: 0.5519 - val_loss: 0.5618\n",
"Epoch 37/50\n",
"55992/55992 [==============================] - 11s 199us/sample - loss: 0.5524 - val_loss: 0.5616\n",
"Epoch 38/50\n",
"55992/55992 [==============================] - 11s 199us/sample - loss: 0.5524 - val_loss: 0.5649\n",
"Epoch 39/50\n",
"55992/55992 [==============================] - 13s 232us/sample - loss: 0.5507 - val_loss: 0.5681\n",
"Epoch 40/50\n",
"55992/55992 [==============================] - 11s 205us/sample - loss: 0.5505 - val_loss: 0.5637\n",
"Epoch 41/50\n",
"55992/55992 [==============================] - 12s 218us/sample - loss: 0.5497 - val_loss: 0.5765\n",
"Epoch 42/50\n",
"55992/55992 [==============================] - 11s 204us/sample - loss: 0.5487 - val_loss: 0.5662\n",
"Epoch 43/50\n",
"55992/55992 [==============================] - 11s 189us/sample - loss: 0.5488 - val_loss: 0.5693\n",
"Epoch 44/50\n",
"55992/55992 [==============================] - 12s 222us/sample - loss: 0.5478 - val_loss: 0.5668\n",
"Epoch 45/50\n",
"55992/55992 [==============================] - 11s 204us/sample - loss: 0.5469 - val_loss: 0.5692\n",
"Epoch 46/50\n",
"55992/55992 [==============================] - 12s 210us/sample - loss: 0.5467 - val_loss: 0.5675\n",
"Epoch 47/50\n",
"55992/55992 [==============================] - 12s 216us/sample - loss: 0.5460 - val_loss: 0.5729\n",
"Epoch 48/50\n",
"55992/55992 [==============================] - 14s 256us/sample - loss: 0.5441 - val_loss: 0.5699\n",
"Epoch 49/50\n",
"55992/55992 [==============================] - 12s 216us/sample - loss: 0.5437 - val_loss: 0.5637\n",
"Epoch 50/50\n",
"55992/55992 [==============================] - 12s 219us/sample - loss: 0.5438 - val_loss: 0.5624\n"
]
}
],
"source": [
"model, predicted, history = run_network(\n",
" X_train, \n",
" y_train, \n",
" X_test,\n",
" layers=[X_train.shape[2], 20, 15, 20, y_train.shape[1]],\n",
" epochs=50)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualise Training\n",
"Lets plot the training loss vs the valiation loss to get a better idea of whether the network is overfitting, underfitting etc. A good resource for interpreting the results can be found [here](http://cs231n.github.io/neural-networks-3/). "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f9ce9a86198>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"fig = plt.figure(figsize=(12, 5))\n",
"plt.plot(history.history['loss'], label='loss')\n",
"plt.plot(history.history['val_loss'], label='val_loss')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Validation\n",
"\n",
"Get error scores for the predicted test data. We provide two measures here, [MSE](https://en.wikipedia.org/wiki/Mean_squared_error) to match what the training was and [MAE](https://en.wikipedia.org/wiki/Mean_absolute_error) which can be more relevant for understanding a forecast accuracy. \n",
"\n",
"We also provide the error scores for each horizon individually. This is very important. We have noticed some articles and worse, libraries, which only provide the **averaged** error score across all the predicted horizons. Naturally, a 1 hour timestep prediction is going to be more accurate than a 24 hour. If we are predicting many horizons then the shorter horizons will be more accurate and reduce the average error score. Therefore its important to evaluate each horizon individually to understand your real predictive power at each step. We can see that the MSE of the test dataset is very similar to the test and validation which is good.\n",
"\n",
"We must also transform the predictions back into actual wind speed values. That means we must unscale and undifference the predictions at the various horizons. We then provide the error scores at these real world scales to again understand the predictions better in a real world context. \n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAE 0.534, MSE 0.504\n",
"MAE 0.617, MSE 0.683 for horizon 1\n",
"MAE 0.474, MSE 0.384 for horizon 6\n",
"MAE 0.450, MSE 0.344 for horizon 12\n",
"MAE 0.593, MSE 0.607 for horizon 24\n"
]
}
],
"source": [
"from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
"\n",
"print(\"MAE {:.3}, MSE {:.3}\".format(\n",
" mean_absolute_error(y_test, predicted),\n",
" mean_squared_error(y_test, predicted)))\n",
"\n",
"for i, horizon in enumerate(horizons):\n",
" print(\"MAE {:.3f}, MSE {:.3f} for horizon {}\".format(\n",
" mean_absolute_error(y_test[:,i], predicted[:,i]),\n",
" mean_squared_error(y_test[:,i], predicted[:,i]),\n",
" horizon))\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Real scale predictions at horizon 1 has MAE 0.857, MSE 1.317, RMSE 1.148\n",
"Real scale predictions at horizon 6 has MAE 1.248, MSE 2.657, RMSE 1.630\n",
"Real scale predictions at horizon 12 has MAE 1.354, MSE 3.109, RMSE 1.763\n",
"Real scale predictions at horizon 24 has MAE 1.492, MSE 3.841, RMSE 1.960\n"
]
}
],
"source": [
"from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
"from math import sqrt\n",
"\n",
"inverse_scale = invert_all_prediction_scaled(scaler, predicted, scaled.columns, horizons)\n",
"\n",
"predicted_signal = invert_all_prediction_differences(\n",
" inverse_scale, \n",
" features['wind_speed'][sequence_offset+row_split:])\n",
"\n",
"for i, horizon in enumerate(horizons):\n",
" a = features['wind_speed'][sequence_offset+row_split+horizon:]\n",
" p = predicted_signal[:-horizon,i]\n",
" \n",
" print(\"Real scale predictions at horizon {:>2} has MAE {:.3f}, MSE {:.3f}, RMSE {:.3f}\".format(\n",
" horizon,\n",
" mean_absolute_error(a, p),\n",
" mean_squared_error(a, p),\n",
" sqrt(mean_squared_error(a, p))))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compare Model with Persistence Forecast\n",
"\n",
"As already mentioned, a [persistence forecast](http://ww2010.atmos.uiuc.edu/(Gh)/guides/mtr/fcst/mth/prst.rxml) is a good baseline comparison for any forecasting model. \n",
"\n",
"We can see that the LSTM model outperforms the persistence method at all horizons. At the 1 hour horizon it is very close as to be expected given such horizons the weather does not change dramatically. At the 6 and 12 hour horizons the LSTM model outperforms the persistence method by a much larger factor (roughly twice as accurate with an MAE value roughly half that of the persistence method). The 24 hour prediction is only just better than the persistence model which shows the cyclical nature of the wind speed.\n",
"\n",
"With better quality data, and more exhaustive tuning and training of the LSTM network we could resonably expect much better results. \n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Persistence Method prediction at horizon 1 has MAE 0.948, MSE 1.685, RMSE 1.298\n",
"Persistence Method prediction at horizon 6 has MAE 2.001, MSE 6.554, RMSE 2.560\n",
"Persistence Method prediction at horizon 12 has MAE 2.325, MSE 8.745, RMSE 2.957\n",
"Persistence Method prediction at horizon 24 has MAE 1.842, MSE 6.024, RMSE 2.454\n"
]
}
],
"source": [
"def evaluate_persistence_forecast(test, horizons):\n",
" for i, horizon in enumerate(horizons):\n",
" a = test[horizon:]\n",
" p = test[:-horizon]\n",
" \n",
" print(\"Persistence Method prediction at horizon {:>2} has MAE {:.3f}, MSE {:.3f}, RMSE {:.3f}\".format(\n",
" horizon,\n",
" mean_absolute_error(a, p),\n",
" mean_squared_error(a, p),\n",
" sqrt(mean_squared_error(a, p))))\n",
"\n",
"\n",
"evaluate_persistence_forecast(\n",
" features['wind_speed'][sequence_offset+row_split:].values, # ensure we have same test set \n",
" horizons)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualising \n",
"\n",
"Finally, we should visualise the predicted wind speeds. We will draw a plot for each time horizon independently. \n",
"\n",
"We can see from the plots that the predictions follow the signal trends quite well. As the signal is very unstable (lots of minor and major adjustments), we can only expect the short term horizon to match it well, as the adjustment to current is minor (remember we're predicting differences). The further out the horizon the less accurate it is in terms of following the exact jagged edges of the original signal which is expected as modelling such a jagged and unstable signal exactly is extermely difficult (if possible due to bad observations). However, as mentioned it is the general trend and strenght that is important and we can see it performs quite well. With better observational data from a more stable source (and height), this apparent discrepancies would start to dissapear and the signal would have a much lower MAE. In particular the power output of turbines would be much smoother than this and hence fit much nicer.\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x1440 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plot_samples=800\n",
"max_horizon = horizons[-1]\n",
"plots = len(horizons)\n",
"\n",
"fig = plt.figure(figsize=(14, 5 * plots))\n",
"fig.suptitle(\"Model Prediction at each Horizon\")\n",
"\n",
"for i, horizon in enumerate(horizons):\n",
" plt.subplot(plots,1,i+1)\n",
" \n",
" len_adjust = max_horizon-horizon # ensure all have same lenght\n",
" \n",
" real = features['wind_speed'][sequence_offset+row_split+horizon+len_adjust:].values\n",
" pred = predicted_signal[len_adjust:-horizon,i]\n",
" \n",
" plt.plot(real[:plot_samples], label='observed')\n",
" plt.plot(pred[:plot_samples], label='predicted')\n",
" plt.title(\"Prediction for {} Hour Horizon\".format(horizon))\n",
" plt.xlabel(\"Hour\")\n",
" plt.ylabel(\"Wind Speed (m/sec)\")\n",
" plt.legend()\n",
" plt.tight_layout()\n",
" \n",
"fig.tight_layout()\n",
"plt.subplots_adjust(top=0.95)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Critique \n",
"\n",
"One very important property we are missing for our predictions, is the confidence our model has in a given prediction - aka credible interval. We can actually extend this model and add a Mixture Density Network ([MDN](http://edwardlib.org/tutorials/mixture-density-network)) as the final layer in the network. MDNs are very useful when combined with neural networks, where the outputs of the neural network are the parameters of the mixture model, rather than direct prediction of the data label. So for each input, you would have a set of mean parameters, a set of standard deviation parameters, and a set of probabilities that the output point would fall into those gaussian distributions ([taken from](http://blog.otoro.net/2015/06/14/mixture-density-networks/)). In a follow up post we will extend our model with an MDN.\n",
"\n",
"Another critique is that we don't do any feature engineering at all. While one of the stated benefits of deep learning in general is its inherent ability to extract latent features, it would be beneficial at least to test out some standard forecasting features such as trend strenghts etc. They may or may not improve the result. Another interesting idea which Uber recently published about their use of LSTMs for forecasting was to use a separate LSTM autoencoder network to create additional features as input to the LSTM model for prediction. See their article [here](https://eng.uber.com/neural-networks/). This is very easy to achieve with Keras. "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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