Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models

Tadeusz A. Grzeszczyk and Michal K. Grzeszczyk
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Tadeusz A. Grzeszczyk: Faculty of Management, Warsaw University of Technology, ul. Narbutta 85, 02-524 Warsaw, Poland
Michal K. Grzeszczyk: Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

Energies, 2022, vol. 15, issue 5, 1-20

Abstract: There is a lot of research on the neural models used for short-term load forecasting (STLF), which is crucial for improving the sustainable operation of energy systems with increasing technical, economic, and environmental requirements. Neural networks are computationally powerful; however, the lack of clear, readable and trustworthy justification of STLF obtained using such models is a serious problem that needs to be tackled. The article proposes an approach based on the local interpretable model-agnostic explanations (LIME) method that supports reliable premises justifying and explaining the forecasts. The use of the proposed approach makes it possible to improve the reliability of heuristic and experimental neural modeling processes, the results of which are difficult to interpret. Explaining the forecasting may facilitate the justification of the selection and the improvement of neural models for STLF, while contributing to a better understanding of the obtained results and broadening the knowledge and experience supporting the enhancement of energy systems security based on reliable forecasts and simplifying dispatch decisions.

Keywords: time-series forecasting; short-term load forecasting; energy forecasting model; neural networks; explainability; local interpretable model-agnostic explanations (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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