Short-term electricity load forecasting with special days: an analysis on parametric and non-parametric methods

2019-01-01
Erisen, Esra
İyigün, Cem
Tanrisever, Fehmi
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Accurately forecasting electricity demand is a key business competency for firms in deregulated electricity markets. Market participants can reap significant financial benefits by improving their electricity load forecasts. Electricity load exhibits a complex time-series structure with nonlinear relationships among the variables. Hence, models with higher capabilities to capture such nonlinear relationships need to be developed and tested. In this paper, we present a parametric and a nonparametric method for short-term load forecasting, and compare the performances of these models for lead times ranging from 1 h to 1 week. In particular, we consider a modified version of the Holt-Winters double seasonal exponential smoothing (m-HWT) model and a nonlinear autoregressive with exogenous inputs (NARX) neural network model. Using hourly load data from the Dutch electricity grid, we carry out an extensive empirical study for five Dutch provinces. Our results indicate that NARX clearly outperforms m-HWT in 1-h-ahead forecasting. Additionally, our modification to HWT leads to a significant improvement in model accuracy especially for special days. Despite its simplicity, m-HWT outperforms NARX for 6- and 12-h-ahead forecasts in general; however, NARX performs better in 24-h-, 48-h- and 1-week-ahead forecasting. In addition, NARX provides drastically lower maximum errors compared to m-HWT, and also clearly outperforms m-HWT in forecasting for short holidays.
Short-term electricity load, Exponential smoothing, Neural networks, NARX, HWT
https://link.springer.com/article/10.1007/s10479-017-2726-6
https://hdl.handle.net/11511/85705
Annals Of Operations Research
Department of Industrial Engineering, Article

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Citation Formats
E. Erisen, C. İyigün, and F. Tanrisever, “Short-term electricity load forecasting with special days: an analysis on parametric and non-parametric methods,” Annals Of Operations Research, pp. 1–34, 2019, Accessed: 00, 2021. [Online]. Available: https://link.springer.com/article/10.1007/s10479-017-2726-6.
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