BUILDING ENERGY DATA ANALYSIS AND PREDICTION WITH RECURRENT NEURAL NETS
Lizhong Wu and Mahesan Niranjan
In this report, we present analysis and prediction of building data using recurrent neural nets. We first explain why a recurrent neural net is chosen by analysing the static and dynamic characteristics of the data, and demonstrating its prediction result. Two techniques are then developed to track the non-stationary state and to catch the long-term memory structure of the data to improve the prediction performance, which cannot be attained with a large recurrent net due to its training difficulty.
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