Abstract
Accurate predictions of solar photovoltaic (PV) power generation at different time horizons are essential for reliable operation of energy management systems. The output power of a PV power plant is dependent on non-linear and intermittent environmental factors, such as solar irradiance, wind speed, relative humidity, etc. Intermittency and randomness of solar PV power effect precision of estimation. To address the challenge, this paper presents a Swarm Decomposition Technique (SWD) based hybrid model as a novel approach for very short-term (15 min) solar PV power generation forecast. The original contribution of the study is to investigate use of SWD for solar data forecast. The solar PV power generation data with hourly resolution obtained from the field (grid connected, 857.08 kWp Akgul Solar PV Power Plant in Turkey) are used to develop and validate the forecast model. Specifically, the analysis showed that the hybrid model with SWD technique provides highly accurate predictions in cloudy periods.
Highlights
Rising energy demand and its related environmental impacts increase the use of renewable energy resources in generation rapidly
In case of having high-frequency components due to cloudy or rainy sky conditions in the solar PV power output, pre-processing step based on decomposition technique made the estimation approach perform better
The most important step of the Swarm Decomposition Technique (SWD) is swarm filtering (SWF) that operates with the swarm-prey hunting approach and produces oscillatory components (OCs) from an element input data
Summary
Rising energy demand and its related environmental impacts increase the use of renewable energy resources in generation rapidly. Since the solar irradiation on the surface shows a highly variable behavior due to cloudiness, it brings difficulties in planning and operation of energy systems with PV power generation [2] Some significant challenges, such as stability, reliability, supply/demand balance, reactive power compensation and frequency response, in power systems may be faced due to large-scale implementation of grid connected PV solar power plants [3], [4]. In [8], a hybrid model based on random vector functional link SARIMA time series analysis and use of wavelet decomposition in pre-processing step for very short-term PV power forecasting was proposed. In case of having high-frequency components due to cloudy or rainy sky conditions in the solar PV power output, pre-processing step based on decomposition technique made the estimation approach perform better.
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