Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting
AbstractArtificial Intelligence (AI) has been widely used in Short-Term Load Forecasting (STLF) in the last 20 years and it has partly displaced older time-series and statistical methods to a second row. However, the STLF problem is very particular and specific to each case and, while there are many papers about AI applications, there is little research determining which features of an STLF system is better suited for a specific data set. In many occasions both classical and modern methods coexist, providing combined forecasts that outperform the individual ones. This paper presents a thorough empirical comparison between Neural Networks (NN) and Autoregressive (AR) models as forecasting engines. The objective of this paper is to determine the circumstances under which each model shows a better performance. It analyzes one of the models currently in use at the National Transport System Operator in Spain, Red Eléctrica de España (REE), which combines both techniques. The parameters that are tested are the availability of historical data, the treatment of exogenous variables, the training frequency and the configuration of the model. The performance of each model is measured as RMSE over a one-year period and analyzed under several factors like special days or extreme temperatures. The AR model has 0.13% lower error than the NN under ideal conditions. However, the NN model performs more accurately under certain stress situations. View Full-Text
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López, M.; Sans, C.; Valero, S.; Senabre, C. Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting. Energies 2018, 11, 2080.
López M, Sans C, Valero S, Senabre C. Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting. Energies. 2018; 11(8):2080.Chicago/Turabian Style
López, Miguel; Sans, Carlos; Valero, Sergio; Senabre, Carolina. 2018. "Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting." Energies 11, no. 8: 2080.
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