Literature number | Model | Innovation point | Scope of application
--- | --- | --- | ---
[12] | Physics Model | Proposed a five-parameter photovoltaic system simulation model combining algebraic and analytical techniques, which has been validated to accurately predict the performance of photovoltaic systems under actual conditions. | Photovoltaic power plant
[13] | Physics Model | Improved prediction accuracy by forecasting solar radiation and temperature using only current solar radiation and temperature values as inputs. | Photovoltaic power plant
[17] | IMWOASVM | Developed a hybrid improved whale optimization algorithm (IMWOA) combined with support vector machine (SVM) model for short-term photovoltaic power prediction. The IMWOA optimized the kernel function parameters and penalty coefficients in SVM, thereby enhancing prediction accuracy. | Photovoltaic power plant
[18] | IMC | Innovatively proposed an improved Markov chain model, which effectively improves the accuracy and applicability of photovoltaic power output probability prediction by combining the conditional probability structure of influencing factors, influencing factor selection based on rough set theory, and K-nearest neighbor similar sample mining. | Photovoltaic power plant
[24] | LSTM | Proposed a multivariate wind power plant ultra-short-term power generation prediction method based on long short-term memory network (LSTM). This method improves the accuracy of wind power prediction by selecting highly correlated multivariate time series through Pearson correlation coefficient and utilizing LSTM network’s dynamic time modeling capability. | Centralized wind farms
[25] | DA-GRU | Proposed a photovoltaic power prediction model based on dual attention mechanism GRU network and similar sequence correction. By introducing feature attention and time attention modules, the model’s ability to extract effective information is enhanced, and the prediction accuracy is improved by correcting the prediction results with future values of similar power sequences. | Centralized Photovoltaic Power Plant
[26] | WOA-PMCI-PCNN-BiLSTM | Proposed a centralized photovoltaic power prediction method combining whale optimization algorithm (WOA) and parallel deep learning model (PMCI-PCNN-BiLSTM). Through similar day clustering, multi-mode decomposition, and multi-channel input, the prediction accuracy and model generalization ability are effectively improved. | Centralized Photovoltaic Power Plant
[27] | K-ISSA-LSTM | Proposed a photovoltaic power prediction method based on short-term power feature clustering of K-ISSA-LSTM. By combining the K-means clustering algorithm and the improved sparrow search algorithm to optimize the hyperparameters of long short-term memory network, the accuracy of photovoltaic power prediction under complex weather conditions is effectively improved. | Centralized Photovoltaic Power Plant
[28] | DC-GAN | Introduced an innovative dynamic convolutional generative adversarial network (DC-GAN) method based on satellite cloud map video frames. By combining radiation data from centralized photovoltaic power stations, spatial correlations of distributed stations, and cloud coverage features, the prediction accuracy is enhanced through model construction. | Distributed Photovoltaic Power Plants
[29] | ANFIS | Proposed a region-based distributed photovoltaic output prediction method based on spatial correlation. By equivalenting distributed stations within the region to virtual centralized stations and using ANFIS method to predict the output of centralized stations, distributed station output prediction is achieved through polynomial fitting. | Distributed PV Cluster