LAPSE:2018.0602
Published Article
LAPSE:2018.0602
Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting
Miguel López, Carlos Sans, Sergio Valero, Carolina Senabre
September 21, 2018
Artificial 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.
Keywords
artificial intelligence (AI), neural networks, short-term load forecasting (STLF)
Suggested Citation
López M, Sans C, Valero S, Senabre C. Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting. (2018). LAPSE:2018.0602
Author Affiliations
López M: Department of Mechanic Engineering and Energy, Universidad Miguel Hernández, 03202 Elx, Alacant, Spain
Sans C: Department of Mechanic Engineering and Energy, Universidad Miguel Hernández, 03202 Elx, Alacant, Spain
Valero S: Department of Mechanic Engineering and Energy, Universidad Miguel Hernández, 03202 Elx, Alacant, Spain
Senabre C: Department of Mechanic Engineering and Energy, Universidad Miguel Hernández, 03202 Elx, Alacant, Spain
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Journal Name
Energies
Volume
11
Issue
8
Article Number
E2080
Year
2018
Publication Date
2018-08-10
Published Version
ISSN
1996-1073
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Original Submission
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PII: en11082080, Publication Type: Journal Article
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LAPSE:2018.0602
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doi:10.3390/en11082080
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Sep 21, 2018
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Calvin Tsay
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