Power substation load forecasting using interpretable transformer-based temporal fusion neural networks

Abstract

Demand forecasting is one of the biggest challenges facing the power system, given that anticipating future consumption is fundamental to guarantee an adequate supply, without wasting resources or overloading the power grid. Despite numerous techniques developed for this purpose, the unstable nature of historical data makes electricity demand time series highly complex, making forecasting a critical and challenging problem. This work develops and explores the predictive potential of the temporal fusion transformer (TFT), an innovative approach capable of performing temporal fusions and forecasts over different time horizons. This architecture features interpretability capabilities, allowing the understanding of which variables and patterns are most relevant to predictions. It can help identify problems, provide insights and improve system reliability. The efficiency of the proposed model is assessed using historical data on the overall load of the New Zealand Electrical Company, which is made up of two thermoelectric power stations and seven substations. The results highlight the TFT as a promising approach since it shows significant indicators in the forecasts, including the mean absolute percentage error of less than 1.5% along 48 h in advance. The obtained performance metrics underscore the accuracy and robustness of the TFT architecture.