Abstract
Affected by many factors, the photovoltaic output power is characterized by nonlinearity, volatility and instability. Therefore, short-term forecasting models are required to have multiple inputs, levels, and categories. In order to solve the above problems and improve the accuracy of predictions, this paper proposes a combined model prediction method based on similar-day clustering and the use of Conjugate Gradient (CG) to improve Deep Belief Network (DBN). The initial method uses fuzzy C-Means Clustering Algorithm (FCM) to perform similar-day clustering on the original data according to the degree of membership. The CG-DBN prediction model is then designed according to the category, with the model ultimately being used to perform the short-term prediction of the PV output power. The proposed scheme uses data from Zhejiang Longyou power station for experimental analysis and verification, and the results were compared with the back propagation neural networks model, Support Vector Machine (SVM) model, and traditional deep belief network. The model’s predicted results are compared. Finally, it is concluded that, in the short-term PV power load forecasting, the prediction performance of the FCM and CG-DBN combination forecast model is better than the above three models and has strong feasibility in short-term PV power forecasting.
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References
Hsiao YH (2017) Household electricity demand forecast based on context information and user daily schedule analysis from meter data. IEEE Trans Industr Inf 11(1):33–43
Meiping Fu, Hongwei Ma, Jianrong Mao (2012) Short-term prediction of photovoltaic power generation based on similar-day and least-sequares support vector machines. Power Syst Prot Control 40(16):65–69
Ardakani FJ, Ardehali MM (2014) Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types. Energy 65(1):452–461
Friedrich L, Afshari A (2015) Short-term forecasting of the Abu Dhabi electricity load using multiple weather variables. Energy Proc 75:3014–3026
Li Z, Ye L, Zhao Y, Song X, Jin J (2016) Short-term wind power prediction based on extreme learning machine with error correction. Prot Control Mod Power Syst 1(1):1
Kaytez F, Taplamacioglu MC, Cam E et al (2015) Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines. Int J Electr Power Energy Syst 67(67):431–438
Mordjaoui M, Haddad S, Medoued A et al (2017) Electric load forecasting by using dynamic neural network. Int J Hydrog Energy 42:17655–17663
Ni Q, Zhuang S, Sheng H et al (2017) An ensemble prediction intervals approach for short-term PV power forecasting[J]. Energies 10:1072–1083
Malvoni M, Giorgi MGD, Congedo PM (2016) Data on Support Vector Machines (SVM) model to forecast photovoltaic power. Data Brief 9(C):13–16
Shi J, Lee WJ, Liu Y et al (2011) Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Trans Ind Appl 48(3):1064–1069
Huabin Z, Mingyu Y (2015) Ultra-short-term forecasting for photovoltaic power output based on least square support vector machine. Mod Electr Power 1:12
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507
Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527e54
Shen F, Chao J, Zhao J (2015) Forecasting exchange rate using deep belief networks and conjugate gradient method. Neurocomputing 167(C):243–253
Kuremoto T, Kimura S, Kobayashi K et al (2014) Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing 137(15):47–56
Chen Y, Huang TT, Liu HL et al (2016) Multi-pose face ensemble classification aided by Gabor features and deep belief nets. Optik Int J Light Electron Opt 127(2):946–954
Dedinec A, Filiposka S, Dedinec A et al (2016) Deep belief network based electricity load forecasting: an analysis of Macedonian case. Energy 115:1688–1700
Qin M, Du Z, Du Z (2017) Red tide time series forecasting by combining ARIMA and deep belief network. Knowl Based Syst 125:39–52
Gao T, Li A, Meng F (2017) Research on data stream clustering based on FCM Algorithm 1. Proc Comput Sci 122:595–602
Acknowledgements
The funding has been recevied from National key R&D Program of China with Grand No. 2017YFB013200.
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Li, Z., Bao, S. & Gao, Z. Short Term Prediction of Photovoltaic Power Based on FCM and CG-DBN Combination. J. Electr. Eng. Technol. 15, 333–341 (2020). https://doi.org/10.1007/s42835-019-00326-3
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DOI: https://doi.org/10.1007/s42835-019-00326-3