Building energy load forecasting using Deep Neural Networks

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.

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

“…Improved performance was reported compared to shallow ANN and support vector machine. The same year (2016), Marino et al [33] presented a novel energy load forecasting methodology using two different deep architectures namely, a standard LSTM and an LSTM with sequence to Sequence architecture that produced promising results. In 2017, Ryu et al [14] proposed a DNN based framework for day-ahead load forecast by training DNNs in two different ways, using a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training.…”

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“…In order to check the implemented LSTM algorithm has been adopted the dataset proposed in [26] and [27]. This dataset contains 2075259 measurements related to period between December 2006 and November 2010 (47 months).…”

Short Term Load Forecasting based on Deep Learning for Smart Grid Applications

“…Especially, the focus of this work is on short term load forecasting, it is a challenging task due to stochastic and non-linear consumption behavior of consumers. Many short term load forecasting models have been proposed in [4]- [11]. However, these models have either accuracy or convergence rate problems.…”