Evolutionary prediction of photovoltaic power plant energy production

Abstract

This paper presents an application of genetic programming to the evolution of fuzzy predictors based on fuzzy information retrieval. The fuzzy predictors are used to estimate the output of a Photovoltaic Power Plant (PVPP). The PVPPs are energy sources with an unstable production of electrical energy. It is necessary to back up the energy produced by the PVPPs for stable electric network operations. An optimal value of backup power can be set with advanced prediction models that can contribute to the robustness of the electric network within the framework of an intelligent power grid. This work extends previous research on evolutionary design of fuzzy PVPP output predictors by the evaluation of the method on a larger data set describing the operations of a real PVPP.

A literature review on estimating of PV-array hourly power under cloudy weather conditions

“…Moreover, Most of published works that are done to forecast models for PV plants in short-term are directed to solar radiation predictions [42][43][44][45], while only a few of them describe models aimed at forecasting hourly power production in PV plants directly [46][47][48][49][50][51][52][53][54]. Besides, the short-term photovoltaic power generation forecasting methods experience forecasting, these methods include electricity elasticity coefficient, integrated power consumption, output and growth rate of consumption, extrapolation forecast and district load density index method.…”

Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity

“…Nevertheless, despite the fact that future contributions of PV plants to the global electricity consumption will be comparable to that corresponding to wind farms, short-term forecasting models for PV plants are in their early stages. Most of the published works corresponding to short-term forecasting models for PV plants are oriented to solar radiation predictions [6][7][8][9], while only a few works describe models aimed at directly forecasting the hourly power production in PV plants [10][11][12][13][14][15][16][17]. Most of these published models are based on artificial neural networks (ANNs).…”

“…Even these forecasted weather values are used in [16] to forecast the hourly power production for all PV plants in a local or regional scale. Genetic programming of evolution of fuzzy rules has been proposed in [17] to estimate the output of a PV plant, allowing the selection of the best forecasting model.…”