Forecasting of Power Quality Parameters Based on Meteorological Data in Small-Scale Household Off-Grid Systems

Ibrahim Salem Jahan, Vojtech Blazek, Stanislav Misak, Vaclav Snasel and Lukas Prokop
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Ibrahim Salem Jahan: ENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic
Vojtech Blazek: ENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic
Stanislav Misak: ENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic
Vaclav Snasel: Computer Science Department, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic
Lukas Prokop: ENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic

Energies, 2022, vol. 15, issue 14, 1-20

Abstract: Off-grid power systems are often used to supply electricity to remote households, cottages, or small industries, comprising small renewable energy systems, typically a photovoltaic plant whose energy supply is stochastic in nature, without electricity distributions. This approach is economically viable and conforms to the requirements of the European Green Deal and the Fit for 55 package. Furthermore, these systems are associated with a lower short circuit power as compared with distribution grid traditional power plants. The power quality parameters (PQPs) of such small-scale off-grid systems are largely determined by the inverter’s ability to handle the impact of a device; however, this makes it difficult to accurately forecast the PQPs. To address this issue, this work compared prediction models for the PQPs as a function of the meteorological conditions regarding the off-grid systems for small-scale households in Central Europe. To this end, seven models—the artificial neural network (ANN), linear regression (LR), interaction linear regression (ILR), quadratic linear regression (QLR), pure quadratic linear regression (PQLR), the bagging decision tree (DT), and the boosting DT—were considered for forecasting four PQPs: frequency, the amplitude of the voltage, total harmonic distortion of the voltage ( T H D u ), and current ( T H D i ). The computation times of these forecasting models and their accuracies were also compared. Each forecasting model was used to forecast the PQPs for three sunny days in August. As a result of the study, the most accurate methods for forecasting are DTs. The ANN requires the longest computational time, and conversely, the LR takes the shortest computational time. Notably, this work aimed to predict poor PQPs that could cause all the equipment in off-grid systems to respond in advance to disturbances. This study is expected to be beneficial for the off-grid systems of small households and the substations included in existing smart grids.

Keywords: forecasting; renewable energy; meteorological data; off-grid system; smart grid (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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