Short-Term Electricity Load Forecasting Based on Temporal Fusion Transformer Model

Abstract

Electricity load forecasting plays an important role in the operation of power systems. Inaccurate forecast would reduce the safety of power supply and affect the economic and social activities as well as national defense and security. In addition, the forecast results also support decision-making on electricity generation and market transactions. Traditional methods such as AR, ARIMA, SARIMA have been widely used to forecast short term electricity load. Recently, load forecasting based on artificial and deep neural networks have shown significant accuracy improvement over traditional statistical models. In this research, a novel recurrent neural network named temporal fusion transformer (TFT) is used to forecast short-term electricity load of Hanoi city. The TFT is a newly developed model and it combines the advantages of several other RNN models such as LSTM and the self-attention mechanism. In addition to historical load data, we use temperature and humidity features, and time features such as calendar month, lunar month, days of the week, hours of the day and holidays. The forecast results of TFT are compared with traditional statistical models as well as well-known RNN models. The compared results show that the proposed method is better than other methods in both MAE and MAPE criteria.