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Preliminary Updates and Installations

(http://markus.com/install-theano-on-aws/)

sudo apt-get update

sudo apt-get -y dist-upgrade  

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ps server

import tensorflow as tf

cluster = tf.train.ClusterSpec({"ps": ['localhost:2222'], "worker": ['localhost:2224','localhost:2225']})
server = tf.train.Server(cluster.as_cluster_def(), job_name='ps', task_index=0)

server.join()

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aaa

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Smart Energy Using Machine Learning

Applications

Solar (PV)

• Solar Forecast
• Prediction of customers who will buy solar (reduce sale cost)
• Insurance/Finance for buying solar

Non-intrusive Load Monitoring (NILM)

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""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

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Enterprise Model Factory

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Simulation Environment For RL

Motivation

To apply (deep) reinforcement learning to other real problems (e.g. energy management, traffic optimization etc.), you first need a simulation environment to learn/train autonomous agent.

Code

• The Arcade Learning Environment
• PyGame-Learning-Environment
• OpenAI Gym