Photovoltaic power prediction method for zero energy consumption buildings based on multi-feature fuzzy clustering and MAOA-ESN

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Due to their high efficiency and compatibility with building integration, photovoltaic (PV) power generation systems are frequently utilized in zero energy buildings. However, in zero energy buildings 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 zero energy buildings. 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 echo state network (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 zero-energy buildings and increases the accuracy of zero-energy building PV power prediction.