Energy Use and Demand Prediction Using Time-Series Deep Learning Forecasting Techniques: Application for a University Campus

Abstract

A growing global impetus has emerged to enhance the sustainability of energy systems and practices. The two popular levers to achieve this goal include increasing the proportion of clean energy in the energy mix and enhancing energy efficiency. The former involves reducing reliance on fossil fuel-based energy sources and increasing the adoption of renewable energy. The latter involves understanding factors that impact the current energy footprint and improving the efficiencies of the process. University campuses comprise many buildings, and it is well-known that buildings have a sizeable energy footprint. Therefore, it is beneficial to understand their energy consumption and identify ways in which this could be further optimised. Furthermore, catering to the energy demand requires appropriate provisioning with significant costs associated with energy procurement on-demand. To address this, it is vital to predict demand in advance accurately. In this article, we elaborate on these two aspects, i.e., analysis of energy consumption and demand forecasting using deep learning-based time series techniques such as Long short-term memory (LSTM), Bi-directional Long short-term memory (BiLSTM), Gated recurrent unit (GRU), and Bidirectional Gated recurrent units (BiGRU). We analyse the different parameter optimisers and history window lengths to select a better hyper-parameter set for accurate energy use and demand prediction. Findings from this study show that the prediction follows the actual demand curve with a minimum RMSE of 65.354 MWh and 65.936 MWh for window sizes of four and six for validation (testing), respectively. The window size six performs better for most time-series algorithms and hyperparameter combinations.