Short term photovoltaic power generation forecasting using RBF neural network

Li, Zhiyong; Zhou, Yunlei; Cheng, Cheng; Li, Yao; Lai, KeXing

doi:10.1109/ccdc.2014.6852641

Cited by 4 publications

(2 citation statements)

References 3 publications

(1 reference statement)

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“…Step 6: If the fitness of the population x j is greater than population x i , the local search will be started and handled by FF algorithm in updating new position according to Equation (13). Otherwise, the population will be handled by the PSO algorithm and it will update the new position according to Equation (16).…”

Section: Proposed Hybridization Of Hfpsomentioning

confidence: 99%

“…Awad et al utilized a K clustering algorithm to determine the center of the activation function for the radial basis function neural network (RBFNN) model [12]. Li et al employed the RBFNN as a forecasting model of PV power in China, and the results showed a lower error percentage during normal and sudden weather changes, showing the ability of the proposed RBFNN to achieve a higher forecasting accuracy [13].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)

et al. 2019

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Solar power generation deals with uncertainty and intermittency issues that lead to some difficulties in controlling the whole grid system due to imbalanced power production and power demand. The forecasting of solar power is an effort in securing the integration of renewable energy into the grid. This work proposes a forecasting model called WT-ANFIS-HFPSO which combines the wavelet transform (WT), adaptive neuro-fuzzy inference system (ANFIS) and hybrid firefly and particle swarm optimization algorithm (HFPSO). In the proposed work, the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data. The HFPSO is the hybridization of the firefly (FF) and particle swarm optimization (PSO) algorithm, which is employed in optimizing the premise parameters of the ANFIS to increase the accuracy of the model. The results obtained from WT-ANFIS-HFPSO are then compared with several other forecasting strategies. From the comparative analysis, the WT-ANFIS-HFPSO showed superior performance in terms of statistical error analysis, confirming its reliability as an excellent forecaster of hourly solar power data.

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Section: Proposed Hybridization Of Hfpsomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)

et al. 2019

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Short‐term photovoltaic power prediction based on MDCM‐GA‐LSTM model

Liu

Wang

et al. 2022

The Journal of Engineering

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In recent years, photo-voltaic (PV) system has become one of the most potential renewable energy power generation technologies because of its many advantages. Considering the influence of the randomness of PV system on the operation and dispatching of power system, the necessity of a comprehensive forecasting model is increased rapidly. This paper proposes a short-term PV power forecasting method based on MDCM-GA-LSTM model to solve the problem of low accuracy of traditional and single forecasting model. First, the meteorological data are pre-processed based on wavelet denoising algorithm to improve the data quality. Second, a meteorological model is established based on genetic algorithm (GA) to optimize long short-term memory (LSTM) neural network. Finally, the network is trained with the modified data of the meteorological model as the input and the actual power as the output, and the power forecasting model is established. In this study, the forecasting accuracy is evaluated based on mean absolute error (MAE) and root mean square error (RMSE). The experimental results demonstrated that the MDCM-GA-LSTM model outperforms the conventional and single model by a difference of about 98.43% of the MAE and 98.97% of the RMSE error.

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Photovoltaic power prediction based on principal component analysis and Support Vector Machine

Song

Qian

et al. 2016

2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)

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Short term photovoltaic power generation forecasting using RBF neural network

Cited by 4 publications

References 3 publications

Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)

Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)

Short‐term photovoltaic power prediction based on MDCM‐GA‐LSTM model

Photovoltaic power prediction based on principal component analysis and Support Vector Machine

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