Short-term forecasting of the output power of a building-integrated photovoltaic system using a metaheuristic approach

Seyedmahmoudian, Mohammadmehdi, Jamei, Elmira, Thirunavukkarasu, Gokul Sidarth, Soon, Tey Kok, Mortimer, Michael, Horan, Ben, Stojcevski, Alex and Mekhilef, Saad 2018, Short-term forecasting of the output power of a building-integrated photovoltaic system using a metaheuristic approach, Energies, vol. 11, no. 5, pp. 1-23, doi: 10.3390/en11051260.

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Title Short-term forecasting of the output power of a building-integrated photovoltaic system using a metaheuristic approach
Author(s) Seyedmahmoudian, Mohammadmehdi
Jamei, Elmira
Thirunavukkarasu, Gokul Sidarth
Soon, Tey Kok
Mortimer, Michael
Horan, BenORCID iD for Horan, Ben
Stojcevski, Alex
Mekhilef, Saad
Journal name Energies
Volume number 11
Issue number 5
Article ID 1260
Start page 1
End page 23
Total pages 23
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2018-05-15
ISSN 1996-1073
Keyword(s) 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
science & technology
energy & fuels
Summary 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.
Language eng
DOI 10.3390/en11051260
Field of Research 09 Engineering
02 Physical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, the authors
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