benchmarking, data-limited, deep learning models, forecasting, statistical model
Abstract
Analysis of electricity consumption profiles is essential for effective load management and the promotion of energy efficiency, thus contributing to sustainable development. However, the limited availability of data, whether due to scarcity, lack of adequate monitoring infrastructure, or privacy issues, poses significant challenges. Despite these limitations, it is essential to investigate the feasibility of using available data for accurate forecasting. This study proposes to forecast electricity demand in university buildings, considering scenarios with limited data. The methodology involves comparative benchmarking of machine learning models, such as recurrent neural networks (RNN), long short-term memory (LSTM) networks, gated recurrent units (GRU), and convolutional neural networks (CNN), with the autoregressive integrated moving average (ARIMA) model. The performance of these models is evaluated using metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), as well as generalizing the methodology to different buildings. The results show that the deep learning models outperform ARIMA in several scenarios, with the GRU model standing out for its accuracy, which is 2.38% higher than ARIMA. The evaluation of the proposed forecasting models provides an objective reference for identifying best practices in electricity demand forecasting, validating their usefulness in practical scenarios, and guiding future investments in research aimed at developing energy management systems and energy efficiency strategies.
