Assessing and Comparing Short Term Load Forecasting Performance

Pekka Koponen, Jussi Ikäheimo, Juha Koskela, Christina Brester and Harri Niska
Additional contact information
Pekka Koponen: VTT, Technical research Centre of Finland, Smart Energy and Built Environment, P.O. Box 1000, FI-02044 Espoo, Finland
Jussi Ikäheimo: VTT, Technical research Centre of Finland, Smart Energy and Built Environment, P.O. Box 1000, FI-02044 Espoo, Finland
Juha Koskela: Department of Electrical Engineering, Tampere University, P.O. Box 1001, FI-33014 Tampere, Finland
Christina Brester: Department of Environmental and Biological Sciences, University of Eastern Finland, P.O. Box 1627, FI-70211 Kuopio, Finland
Harri Niska: Department of Environmental and Biological Sciences, University of Eastern Finland, P.O. Box 1627, FI-70211 Kuopio, Finland

Energies, 2020, vol. 13, issue 8, 1-17

Abstract: When identifying and comparing forecasting models, there may be a risk that poorly selected criteria could lead to wrong conclusions. Thus, it is important to know how sensitive the results are to the selection of criteria. This contribution aims to study the sensitivity of the identification and comparison results to the choice of criteria. It compares typically applied criteria for tuning and performance assessment of load forecasting methods with estimated costs caused by the forecasting errors. The focus is on short-term forecasting of the loads of energy systems. The estimated costs comprise electricity market costs and network costs. We estimate the electricity market costs by assuming that the forecasting errors cause balancing errors and consequently balancing costs to the market actors. The forecasting errors cause network costs by overloading network components thus increasing losses and reducing the component lifetime or alternatively increase operational margins to avoid those overloads. The lifetime loss of insulators, and thus also the components, is caused by heating according to the law of Arrhenius. We also study consumer costs. The results support the assumption that there is a need to develop and use additional and case-specific performance criteria for electricity load forecasting.

Keywords: short term load forecasting; performance criteria; power systems; cost analysis (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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