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

Abstract

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.

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Correspondence to Cem Iyigun.

Appendices

Appendix A: M-HWT model parameters

See Tables 7, 8, 9, 10.

Table 7 Parameters for m-HWT models of Maastricht data set
Table 8 Parameters for m-HWT models of Limburg data set
Table 9 Parameters for m-HWT models of Friesland data set
Table 10 Parameters for m-HWT models of Noord data set

Appendix B: NARX architectures

See Tables 11, 12, 13.

Table 11 Maastricht data set model architectures for different forecasting horizons
Table 12 Limburg data set model architectures for different forecasting horizons
Table 13 Friesland data set model architectures for different forecasting horizons

Appendix C: Model performance for different datasets

See Tables 14, 15, 16.

Table 14 Performances of NARX, HWT and m-HWT for different day types in terms of MAPE for Limburg data set
Table 15 Performances of NARX, HWT and m-HWT for different day types in terms of MAPE for Friesland data set
Table 16 Performances of NARX, HWT and m-HWT for different day types in terms of MAPE for Maastricht data set

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Erişen, E., Iyigun, C. & Tanrısever, F. Short-term electricity load forecasting with special days: an analysis on parametric and non-parametric methods. Ann Oper Res (2017). https://doi.org/10.1007/s10479-017-2726-6

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  • DOI: https://doi.org/10.1007/s10479-017-2726-6

Keywords

  • Short-term electricity load
  • Exponential smoothing
  • Neural networks
  • NARX
  • HWT