- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting
摘要： As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.
关键词： ensemble,decision tree,bagging,Light GBM,lagged data,machine learning,random forest,XGBoost,photovoltaic power forecasting
更新于2025-09-23 15:21:01
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[IEEE 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) - Busan, Korea (South) (2020.2.19-2020.2.22)] 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) - Transfer Learning for Photovoltaic Power Forecasting with Long Short-Term Memory Neural Network
摘要： Data-driven modeling is one of the research hotspots of photovoltaic (PV) power prediction. However, for some newly built PV power plants, there are not enough historical data to train an accurate model. Therefore, constructing a forecasting model for the PV plants lacking historical data is an urgent problem to be solved. In this paper, we propose a method to transfer the knowledge obtained from historical solar irradiance data to the output prediction. Firstly, the based on hyperparameters of the long short-term memory neural network (LSTM) are optimized and the weights in the neurons are pre-trained, then fine-tuning the deep transfer model with PV output data. In this way, knowledge can be transferred to PV output data. The from solar experimental results show that the proposed method can significantly reduce the prediction error.
关键词： Long short-term memory,Transfer learning,Photovoltaic power forecasting,Hyperparameter optimization,Data mining
更新于2025-09-23 15:21:01
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Photovoltaic power forecasting based LSTM-Convolutional Network
摘要： The volatile and intermittent nature of solar energy itself presents a significant challenge in integrating it into existing energy systems. Accurate photovoltaic power prediction plays an important role in solving this problem. With the development of deep learning, more and more scholars have applied the deep learning model to time series prediction and achieved very good results. In this paper, a hybrid deep learning model (LSTM-Convolutional Network) is proposed and applied to photovoltaic power prediction. In the proposed hybrid prediction model, the temporal features of the data are extracted first by the long-short term memory network, and then the spatial features of the data are extracted by the convolutional neural network model. In order to show the superior performance of the proposed hybrid prediction model, the prediction results of the hybrid model are compared with those of the single model (long-short term memory network, convolutional neural network) and the hybrid network (Convolutional-LSTM Network) model, and the results of eight error evaluation indexes are given. The results show that the hybrid prediction model has better prediction effect than the single prediction model, and the proposed hybrid model (extract the temporal characteristics of data first, and then extract the spatial characteristics of data) is better than Convolutional-LSTM Network (extract the spatial characteristics of data first, and then extract the temporal characteristics of data).
关键词： Convolutional-LSTM network,LSTM-Convolutional network,Photovoltaic power forecasting,Convolutional neural network,Deep learning,Long-short term memory
更新于2025-09-16 10:30:52
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[IEEE 2017 International Renewable and Sustainable Energy Conference (IRSEC) - Tangier (2017.12.4-2017.12.7)] 2017 International Renewable and Sustainable Energy Conference (IRSEC) - Data Driven Model for Short Term PV Power Forecasting using Least Square Support Vector Regression
摘要： This paper presents an off-line model for forecasting photovoltaic power. This model is suitable to provide short-term forecasts without the need of Numerical Weather predictions data. This is interesting especially for power system operators as well as for individuals who do not have access to weather data and forecasts. In this paper we investigate the influence of an additional input parameter to the accuracy of an already tested and validated offline model. To rectify the performances of our models we will compare their performances with a usual persistent model. The results of simulation shows the benefits of adding this input to improve the accuracy of our PV forecasting model.
关键词： Photovoltaic Power,Forecasting,Least Square Support Vector Regression,Smart Grid,Grid Management,Machine Learning
更新于2025-09-10 09:29:36