Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting

Athanasios I. Salamanis, Georgia Xanthopoulou, Napoleon Bezas, Christos Timplalexis, Angelina D. Bintoudi, Lampros Zyglakis, Apostolos C. Tsolakis, Dimosthenis Ioannidis, Dionysios Kehagias and Dimitrios Tzovaras
Additional contact information
Athanasios I. Salamanis: Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece
Georgia Xanthopoulou: Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece
Napoleon Bezas: Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece
Christos Timplalexis: Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece
Angelina D. Bintoudi: Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece
Lampros Zyglakis: Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece
Apostolos C. Tsolakis: Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece
Dimosthenis Ioannidis: Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece
Dionysios Kehagias: Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece
Dimitrios Tzovaras: Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece

Energies, 2020, vol. 13, issue 22, 1-31

Abstract: Accurately forecasting power generation in photovoltaic (PV) installations is a challenging task, due to the volatile and highly intermittent nature of solar-based renewable energy sources. In recent years, several PV power generation forecasting models have been proposed in the relevant literature. However, there is no consensus regarding which models perform better in which cases. Moreover, literature lacks of works presenting detailed experimental evaluations of different types of models on the same data and forecasting conditions. This paper attempts to fill in this gap by presenting a comprehensive benchmarking framework for several analytical, data-based and hybrid models for multi-step short-term PV power generation forecasting. All models were evaluated on the same real PV power generation data, gathered from the realisation of a small scale pilot site in Thessaloniki, Greece. The models predicted PV power generation on multiple horizons, namely for 15 min, 30 min, 60 min, 120 min and 180 min ahead of time. Based on the analysis of the experimental results we identify the cases, in which specific models (or types of models) perform better compared to others, and explain the rationale behind those model performances.

Keywords: power forecasting; photovoltaic systems; analytical models; data-based models; hybrid models; benchmarking (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 (5)

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