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

Athanasios I Salamanis,Georgia Xanthopoulou,Christos Timplalexis,Dionysios Kehagias,Napoleon Bezas,Dimosthenis Ioannidis,Angelina D Bintoudi,Lampros Zyglakis,Dimitrios Tzovaras,Apostolos C Tsolakis

doi:10.3390/en13225978

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.

Highlights

Photovoltaic (PV) power generation is constantly gaining ground as a renewable energy source (RES) within the energy market
The long short-term memory (LSTM) model has the best performance for 1 step ahead, the deep neural networks (DNN) model for 2, the HGBT model for 4 and the Analytical PV Forecast (AI-PVF) model for 8 and 12
The mean absolute percentage error (MAPE) value of the best performing model (i.e., AI-PVF) forecasting for 1 step ahead in the sunny days subperiod is 2.898%, while the corresponding value of the best performing model (i.e., LSTM) in the cloudy days subperiod is 22.582%. This is demonstrated by the order of magnitude of the errors, where in the sunny days subperiod it is at the level of at most while in the cloudy days is at the level of

Summary

Introduction

Photovoltaic (PV) power generation is constantly gaining ground as a renewable energy source (RES) within the energy market. In 2018, a capacity over 500 GW providing around. By 2019, the current estimation is that the PV capacity will reach 650 GW providing for the 4% of the global production [2]. Future scenarios for RES systems penetration in the market are even more optimistic, with some countries aiming to reach 100% [3] in the decades, towards complete decarbonization. PV production is volatile and intermittent, due to its direct dependency on weather conditions. This introduces considerable uncertainty to the system operation, which is translated into significant risks to the stability and reliability of both the transmission and distribution networks [4]

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Results

Conclusion