<p>I'm working on the same thing. Tensorflow with a deep neural network is all that's needed. I believe convolutional LSTM neural networks can take weather data as an input and give a prediction as an output. You just need historical data to train it. Maybe an almanac combined with forecasts and measurement data at the time of forecast.</p>

<p>Research has shown that Convolutional Long Short Term Memory (ConvLSTM) algorithm is more accurate at predicting precicipation than FC_LSTM and current state of the art ROVER algorithms. Here's the paper: <a href="https://arxiv.org/abs/1506.04214" rel="nofollow noreferrer">https://arxiv.org/abs/1506.04214</a></p>

<p>Research also shows that wind can be predicted using NOAA's data and the machine learning algorithms predict better than NOAA. The paper is here: <a href="http://aditya-grover.github.io/files/publications/kdd15.pdf" rel="nofollow noreferrer">http://aditya-grover.github.io/files/publications/kdd15.pdf</a></p>

<p>And finally research has shown that temperature, humidity, and wind can be accurately predicted out to 72hrs using a 15 year data period of hourly measurements. Everything needed to train an algo is spelled out in this article: Sequence to Sequence Weather Forecasting with Long
Short-Term Memory Recurrent Neural Networks, International Journal of Computer Applications (0975 - 8887)
Volume 143 - No.11, June 2016</p>
Demand forecast using weather data
<p>I am trying to write a demand forecast that considers weather data (temperature, pressure, humidity) one by one (or all together). I want to use machine learning algorithms to do so. I used Linear Regression to do the demand forecast previously not considering the weather data, now that I have weather data I am not sure which machine learning algorithm should I use to do the task? I am newbie in Machine Learning and would be grateful if you help me figuring out this problem.</p>

<p>I am using Python for my code, so if you can direct me to use any specific module that would be great.</p>