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[IEEE 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2) - Beijing, China (2018.10.20-2018.10.22)] 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2) - A Power Prediction System for Photo-Voltaic Power Plants
摘要： In this paper, a frequency domain identification-based power prediction system is developed for the photo-voltaic (PV) power plant. A first-order plus dead time (FOPDT) model of the PV power plant is first identified using the daily PV power plant operating data. Given the identified FOPDT model, we do discretization to obtain an FOPDT-based iterative calculation formula for the PV power prediction. Finally, a portable Power Prediction software of Python is developed using the resulting iterative calculation formula. In numerical experiments, we showcase the effectiveness of the Power Prediction software by applying it to real data.
关键词： Solar Irradiance,Python,Photovoltaic Power plant,Portable,Frequency Identification,Power Prediction
更新于2025-09-23 15:23:52
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[IEEE 2019 4th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON) - Bangkok, Thailand (2019.12.11-2019.12.13)] 2019 4th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON) - Impact of Correlation-based Feature Selection on Photovoltaic Power Prediction
摘要： This paper empirically presents the impact of the correlation-based feature selection on the accuracy of the photovoltaic (PV) power prediction, and then selects the weather variables that maximize prediction accuracy. To this end, the experiments are conducted using the weather dataset consisting of eighteen weather variables (i.e., features). For experiments, we first calculate a correlation coefficient of each weather variable by analyzing the correlation between PV power and each weather variable. Then, we create the subsets of weather variables considering the absolute value of correlation coefficient and generate the multiple prediction models using the created subsets. Finally, the accuracy of the generated prediction models is compared with each other to find the most accurate prediction model. The experiment results provide a reference guideline for selecting the weather variables that maximize the accuracy of PV power prediction.
关键词： Correlation coefficient,photovoltaics power prediction,weather variables,feature selection,machine learning
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Macao, Macao (2019.12.1-2019.12.4)] 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Photovoltaic Power Generation Prediction Using Data Clustering and Parameter Optimization
摘要： With the rapid development of the photovoltaic industry, photovoltaic power forecasting has become an urgent problem to be solved. In this paper, a method for predicting photovoltaic power based on data clustering and parameter optimization is proposed. The proposed method can be implemented as follows: firstly, the meteorological feature to be collected is determined by analyzing the physical model of the photovoltaic cell and the collected numerical weather information is divided into a set of categories by K-means. Then, the BP neural network is adopted and trained for individual categories, and an adaptive parameter optimization method is proposed to prevent model from local optimum. In the end, the proposed method is compared with other models to verify its effectiveness.
关键词： Photovoltaic Power Prediction,BP Neural Network,Data Clustering,Parameter Optimization
更新于2025-09-19 17:13:59
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Prediction short-term photovoltaic power using improved chicken swarm optimizer - Extreme learning machine model
摘要： Photovoltaic power generation is greatly affected by weather conditions while the photovoltaic power has a certain negative impact on the power grid. The power sector takes certain measures to abandon photovoltaic power generation, thus limiting the development of clean energy power generation. This study is to propose an accurate short-term photovoltaic power prediction method. A new short-term photovoltaic power output prediction model is proposed Based on extreme learning machine and intelligent optimizer. Firstly, the input of the model is determined by correlation coef?cient method. Then the chicken swarm optimizer is improved to strengthen the convergence. Secondly, the improved chicken swarm optimizer is used to optimize the weights and the extreme learning machine thresholds to improve the prediction effect. Finally, the improved chicken swarm optimizer extreme learning machine model is used to predict the photovoltaic power under different weather conditions. The testing results show that the average mean absolute percentage error and root mean square error of improved chicken swarm optimizer - extreme learning machine model are 5.54% and 3.08%. The proposed method is of great signi?cance for the economic dispatch of power systems and the development of clean energy.
关键词： Extreme learning machine,Model-driven method,Photovoltaic power generation,Intelligent optimizer,Power prediction
更新于2025-09-16 10:30:52
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Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction
摘要： The capability of accurately predicting the Solar Photovoltaic (PV) power productions is crucial to effectively control and manage the electrical grid. In this regard, the objective of this work is to propose an efficient Artificial Neural Network (ANN) model in which 10 different learning algorithms (i.e., different in the way in which the adjustment on the ANN internal parameters is formulated to effectively map the inputs to the outputs) and 23 different training datasets (i.e., different combinations of the real-time weather variables and the PV power production data) are investigated for accurate 1 day-ahead power production predictions with short computational time. In particular, the correlations between different combinations of the historical wind speed, ambient temperature, global solar radiation, PV power productions, and the time stamp of the year are examined for developing an efficient solar PV power production prediction model. The investigation is carried out on a 231 kWac grid-connected solar PV system located in Jordan. An ANN that receives in input the whole historical weather variables and PV power productions, and the time stamp of the year accompanied with Levenberg-Marquardt (LM) learning algorithm is found to provide the most accurate predictions with less computational efforts. Specifically, an enhancement reaches up to 15, 1, and 5% for the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) performance metrics, respectively, compared to the Persistence prediction model of literature.
关键词： learning algorithms,training datasets,solar photovoltaic,persistence,Artificial Neural Networks,power prediction
更新于2025-09-12 10:27:22
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[IEEE 2018 IEEE International Conference on Information and Automation (ICIA) - Wuyishan, China (2018.8.11-2018.8.13)] 2018 IEEE International Conference on Information and Automation (ICIA) - Research on Photovoltaic Power Generation Power Prediction Algorithm Based on Component Aging
摘要： The timely safety improvement of power system scheduling and the reasonable arrangement of various forms of power supply work are realized by the accurate photovoltaic power forecasting.Therefore, in order to achieve a more accurate forecast of photovoltaic power, In this paper, the photovoltaic power generation system based on the theory of the similar day output power of the defects in the prediction algorithm was improved, in the new algorithm consider the aging of components, so it is concluded that the power prediction more accurate.
关键词： Photovoltaic power generation,Prediction algorithm,Photovoltaic panel life cycle,Power prediction
更新于2025-09-11 14:15:04
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An adaptive hybrid model for day-ahead photovoltaic output power prediction
摘要： Accurate and stable photovoltaic (PV) output power prediction is important for the secure, stable and economic operation of power gird. However, due to the indirectness, randomness and volatility of solar energy, accurate and stable PV output power prediction has become a very challenging issue. To obtain a more accurate and stable prediction results, an adaptive hybrid model combined with improved variational mode decomposition (IVMD), autoregressive integrated moving average (ARIMA) and improved deep belief network (IDBN) is developed to predict day-ahead PV output power. First, the original PV output power is decomposed into some regular and irregular components by IVMD. Second, the regular components are predicted by ARIMA, while irregular components are predicted by IDBN. Third, the ?nal forecasting results is obtained by summing the prediction results of each component. The prediction performance is validated by comparing with some other models. Experimental results illustrate that the presented model can improve the prediction performance of PV output power than other models.
关键词： PV output power prediction,ARIMA,IDBN,IVMD
更新于2025-09-11 14:15:04