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• W4377990941 abstract "Due to their high efficiency and compatibility with building integration, photovoltaic (PV) power generation systems are frequently utilized in zero energy buildings (ZEBs). However, in ZEBs with a high proportion of PV systems, the instability of the PV power generation has resulted in various issues, including a significant influence on the primary grid and inadequate photovoltaic power generation utilization. Accurately predicting PV power is crucial for buildings to utilize solar energy and achieve zero energy consumption fully. Consequently, this paper proposed a novel hybrid short-term PV power prediction method based on an echo state network, fuzzy clustering, similar day, and an intelligent optimization algorithm to improve the accuracy of PV power prediction for ZEBs. First, the dataset is partitioned using an improved fuzzy C-mean clustering method (FCM), effectively reducing the effect of PV diversity on the model. Subsequently, an improved similarity day algorithm is employed to select the training data of Echo State Networks (ESN) to minimize the effect of randomness. The improved similarly day algorithm reduces the effect of randomness by retaining valuable samples. Moreover, the ESN parameters are sought using a multi-strategy collaborative improved Archimedean optimization algorithm (MAOA) to avoid poor prediction due to improper parameter settings. Finally, the model is evaluated using historical Australian PV data. The results show that the method effectively reduces the impacts of stochasticity and nonlinearity in the PV power generation process of ZEBs and increases the accuracy of ZEBs PV power prediction." @default.
• W4377990941 created "2023-05-25" @default.
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• W4377990941 date "2023-09-01" @default.
• W4377990941 modified "2023-10-08" @default.
• W4377990941 title "Photovoltaic power prediction method for zero energy consumption buildings based on multi-feature fuzzy clustering and MAOA-ESN" @default.
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• W4377990941 doi "https://doi.org/10.1016/j.jobe.2023.106922" @default.
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