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Research on Power Consumption Data Prediction of Distributed Photovoltaic Power Station

Junfeng Yao, Chun Xiao, Junbo Hao, Xiaoxia Yang

Abstract


At present, the construction of distributed photovoltaic power stations in China lacks systematic and comprehensive preliminary planning; The construction cost exceeded the estimated estimate. After the completion of the project economic benefits cannot reach the expected income, project operating costs exceed expectations and other problems. In order to solve these problems, it is urgent to reasonably forecast the electricity consumption data of distributed photovoltaic power stations. Therefore, in order to solve these problems, a reliable model is established to predict the electricity consumption data of distributed photovoltaic power stations, and the indirect prediction method is used to forecast, that is, the irradiance of medium and long-term time scales is predicted by historical meteorological data, and then the system electricity consumption data is obtained. Among them, the model used is the Long short-term memory (LSTM) neural network model. Under the effect of this model, the electricity consumption data prediction of distributed photovoltaic power stations is carried out. The result shows that the MAPE of monthly prediction is 3.5%, and the annual prediction is 1.1%, which has ideal prediction accuracy and can achieve better prediction effect. This indirect forecasting method breaks the shackles of traditional forecasting methods, avoids the problems of data collection and other aspects, and is a new development trend and the performance of scientific and technological progress, which is conducive to the development of distributed photovoltaic power stations.

 

Doi: 10.28991/HIJ-2024-05-04-05

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Keywords


Distributed Photovoltaic Power Station; Forecast; Electricity Consumption Data; LSTM.

References


Jovijari, F., & Mehrpooya, M. (2024). Development of crude oil desalination unit by using solar flat plate collectors. Applied Thermal Engineering, 239, 122110. doi:10.1016/j.applthermaleng.2023.122110.

Awogbemi, O., & Von Kallon, D. V. (2023). Towards the development of underutilized renewable energy resources in achieving carbon neutrality. Fuel Communications, 100099.doi:10.1016/j.jfueco.2023.100099.

Rafiq, M., Mahr, M. S., Imran, R., Shaban, M., Al-Saeedi, S. I., Hasanin, T. H. A., Salim, M., & Ibrahim, M. A. A. (2023). Towards Development of High-Performance Perovskite Solar Cells Based on Pyrrole Materials for Hole Transport Layer by Using Computational Approach. Journal of Computational Biophysics and Chemistry, 22(8), 1097–1113. doi:10.1142/S2737416523420127.

Charbonnier, F., Morstyn, T., & McCulloch, M. (2024). Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles. MethodsX, 12, 102618. doi:10.1016/j.mex.2024.102618.

Liang, Y., Li, P., Su, W., Li, W., & Xu, W. (2024). Development of green data center by configuring photovoltaic power generation and compressed air energy storage systems. Energy, 292. doi:10.1016/j.energy.2024.130516.

Qiu, Z., Tian, Y., Luo, Y., Gu, T., & Liu, H. (2024). Wind and Photovoltaic Power Generation Forecasting for Virtual Power Plants Based on the Fusion of Improved K-Means Cluster Analysis and Deep Learning. Sustainability, 16(23), 10740. doi:10.3390/su162310740.

Yu, X. P., Li, P., Zhang, Y., Li, H., Yang, M., Zheng, Y., & Xue, M. (2022, November). Research on New Energy Generation Market Transaction Based on Sales Risk Control Strategy. 2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2), 2008-2014. doi:10.1109/EI256261.2022.10117401.

Lu, P., Ye, L., Zhong, W., Qu, Y., Zhai, B., Tang, Y., & Zhao, Y. (2020). A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy. Journal of Cleaner Production, 254, 119993. doi:10.1016/j.jclepro.2020.119993.

Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de-Pison, F. J., & Antonanzas-Torres, F. (2016). Review of photovoltaic power forecasting. Solar energy, 136, 78-111. doi:10.1016/j.solener.2016.06.069.

Zhang, W., Li, Q., & He, Q. (2022). Application of machine learning methods in photovoltaic output power prediction: A review. Journal of Renewable and Sustainable Energy, 14(2), 022701. doi:10.1063/5.0082629.

Al-Dahidi, S., Madhiarasan, M., Al-Ghussain, L., Abubaker, A. M., Ahmad, A. D., Alrbai, M., ... & Zio, E. (2024). Forecasting solar photovoltaic power production: a comprehensive review and innovative data-driven modeling framework. Energies, 17(16), 4145. doi:10.3390/en17164145.

Herraiz, Á. H., Marugán, A. P., & Márquez, F. P. G. (2020). Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure. Renewable Energy, 153, 334-348. doi:10.1016/j.renene.2020.01.148

Bo, G., Chao, M., Chongbiao, Z., Weijie, Q., Chao, F., & Chao, Z. (2023). Output Forecast of Distributed Photovoltaic Power Generation based on Spatial-Temporal Graph Neural Network. Journal of Electric Power Systems and Automation, 35, 125–133.

Wang, S., Yan, S., Li, H., Zhang, T., Jiang, W., Yang, B., ... & Wang, J. (2024). Short-term prediction of photovoltaic power based on quadratic decomposition and residual correction. Electric Power Systems Research, 236, 110968. doi:10.1016/j.epsr.2024.110968.

Hou, L., Ding, H., Liu, Y., & Wang, S. (2022). Evaluation and suggestion on the subsidy policies for rural clean heating in winter in the Beijing-Tianjin-Hebei region. Energy and Buildings, 274, 112456. doi:10.1016/j.enbuild.2022.112456.

Wang, Y., Chen, L., & Shi, X. (2023). Prediction of scrap volume and recyclable resource potential of distributed photovoltaic power generation equipment in the Beijing-Tianjin-Hebei region. Resources Science, 45(10), 2076–2088. doi:10.18402/resci.2023.10.12.

Molina, M. G., & Espejo, E. J. (2014). Modeling and simulation of grid-connected photovoltaic energy conversion systems. International Journal of Hydrogen Energy, 39(16), 8702-8707. doi:10.1016/j.ijhydene.2013.12.048.

Zhang, C., Yan, X., & Nie, J. (2023). Economic analysis of whole-county PV projects in China considering environmental benefits. Sustainable Production and Consumption, 40, 516-531. doi:10.1016/j.spc.2023.07.020.

Li, J., Wang, P., Dong, H., & Shen, J. (2022). Multi/many-objective evolutionary algorithm assisted by radial basis function models for expensive optimization. Applied Soft Computing, 122, 108798. doi:10.1016/j.asoc.2022.108798.

de Souza, L. P., Sanches-Neto, F. O., Junior, G. M. Y., Ramos, B., Lastre-Acosta, A. M., Carvalho-Silva, V. H., & Teixeira, A. C. S. C. (2022). Photochemical environmental persistence of venlafaxine in an urban water reservoir: A combined experimental and computational investigation. Process Safety and Environmental Protection, 166, 478-490. doi:10.1016/j.psep.2022.08.049.


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DOI: 10.28991/HIJ-2024-05-04-05

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