Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach

Mehdi Seyedmahmoudian, Elmira Jamei, Gokul Sidarth Thirunavukkarasu, Tey Kok Soon, Michael Mortimer, Ben Horan, Alex Stojcevski and Saad Mekhilef
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Mehdi Seyedmahmoudian: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne VIC 3122, Victoria, Australia
Elmira Jamei: College of Engineering and Science, Victoria University, Melbourne VIC 3011, Victoria, Australia
Gokul Sidarth Thirunavukkarasu: School of Engineering, Deakin University, Geelong VIC 3216, Victoria, Australia
Tey Kok Soon: Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Michael Mortimer: School of Engineering, Deakin University, Geelong VIC 3216, Victoria, Australia
Ben Horan: School of Engineering, Deakin University, Geelong VIC 3216, Victoria, Australia
Alex Stojcevski: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne VIC 3122, Victoria, Australia
Saad Mekhilef: Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia

Energies, 2018, vol. 11, issue 5, 1-23

Abstract: The rapidly increasing use of renewable energy resources in power generation systems in recent years has accentuated the need to find an optimum and efficient scheme for forecasting meteorological parameters, such as solar radiation, temperature, wind speed, and sun exposure. Integrating wind power prediction systems into electrical grids has witnessed a powerful economic impact, along with the supply and demand balance of the power generation scheme. Academic interest in formulating accurate forecasting models of the energy yields of solar energy systems has significantly increased around the world. This significant rise has contributed to the increase in the share of solar power, which is evident from the power grids set up in Germany (5 GW) and Bavaria. The Spanish government has also taken initiative measures to develop the use of renewable energy, by providing incentives for the accurate day-ahead forecasting. Forecasting solar power outputs aids the critical components of the energy market, such as the management, scheduling, and decision making related to the distribution of the generated power. In the current study, a mathematical forecasting model, optimized using differential evolution and the particle swarm optimization (DEPSO) technique utilized for the short-term photovoltaic (PV) power output forecasting of the PV system located at Deakin University (Victoria, Australia), is proposed. A hybrid self-energized datalogging system is utilized in this setup to monitor the PV data along with the local environmental parameters used in the proposed forecasting model. A comparison study is carried out evaluating the standard particle swarm optimization (PSO) and differential evolution (DE), with the proposed DEPSO under three different time horizons (1-h, 2-h, and 4-h). Results of the 1-h time horizon shows that the root mean square error (RMSE), mean relative error (MRE), mean absolute error (MAE), mean bias error (MBE), weekly mean error (WME), and variance of the prediction errors (VAR) of the DEPSO based forecasting is 4.4%, 3.1%, 0.03, ?1.63, 0.16, and 0.01, respectively. Results demonstrate that the proposed DEPSO approach is more efficient and accurate compared with the PSO and DE.

Keywords: differential evolution and the particle swarm optimization; hybrid meta-heuristic approach; mean absolute error; mean bias error; mean relative error; root mean square error; variance of the prediction errors; weekly mean error (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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