Building Energy Load Forecasting using Deep Neural Networks

TL;DR

This paper introduces a deep neural network approach using LSTM architectures for energy load forecasting, demonstrating the effectiveness of sequence-to-sequence models at different data resolutions, with implications for smarter energy management.

Contribution

The paper presents a novel application of LSTM and sequence-to-sequence architectures for energy load forecasting, comparing their performance on different data resolutions.

Findings

Standard LSTM performs well at one-hour resolution.

LSTM-based Sequence to Sequence architecture performs well at both resolutions.

The proposed methods are comparable to existing deep learning approaches in energy forecasting.

Abstract

Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past, however has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically Long Short Term Memory (LSTM) algorithms. The presented work investigates two variants of the LSTM: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures where trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.