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Detection, mining and forecasting of impact load in power load forecasting
Applied Mathematics and Computation, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jianzhou Wang, Zhixin Ma, Lian Li
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The Forecasting Power of the CAPM
SSRN Electronic Journal, 2019The relationship between risk and an expected return of an investment lays in the capital asset pricing model (CAPM). The main objective is to capitalize on the valuation of future benefits. The CAPM will thus provide insight into the appropriate rate of return of an asset for better decision making. CAPM ensures a relatively accurate prediction of the
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Ensemble Methods for Solar Power Forecasting
2020 International Joint Conference on Neural Networks (IJCNN), 2020We consider the task of predicting the solar power generated by a photovoltaic system, one-step ahead, from previous half-hourly photovoltaic power data. We propose a range of strategies for constructing static and dynamic heterogeneous ensembles and conduct an extensive evaluation using data for two years from two Australian solar power plants.
Zezhou Chen, Irena Koprinska
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Forecasting System for Solar-Power Generation
2021Environmental protection is a highly concerned and thought-provoking issue, and the way of generating electricity has become a major conundrum for all mankind. Renewable or green energy is an ideal solution for environmentally friendly (eco-friendly) power generation.
Jia-Hao Syu, Chi-Fang Chao, Mu-En Wu
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Application of Grey Forecasting Model to Load Forecasting of Power System
2011The grey GM (1, 1) model is a kind of more effective load forecasting model, however, because power load has diversity, causing some variation is larger, the load forecasting error cannot match the requirements. Precision in practical application has certain limitations.
Yan Yan +4 more
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Solar Power Generation Forecasting Service
2019 International Conference on Information and Communication Technology Convergence (ICTC), 2019Starting in February 2019, the small-scale electricity brokerage market was opened and electricity aggregators were allowed to sell electricity markets. The ability of electricity aggregators to accurately predict the generation of electricity is competitive in electricity market and Renewable Energy Certificates (REC) transactions.
Jeong-In Lee +3 more
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Forecasting Methods in Electric Power Sector
International Journal of Energy Optimization and Engineering, 2018Electric power plays a vibrant role in economic growth and development of a region. There is a strong co-relation between the human development index and per capita electricity consumption. Providing adequate energy of desired quality in various forms in a sustainable manner and at a competitive price is one of the biggest challenges.
Sujit Kumar Panda +2 more
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An overview on wind power forecasting methods
2015 International Conference on Machine Learning and Cybernetics (ICMLC), 2015With the continually increasing growth in wind generation being integrated into the electric networks, it brings about significant challenges for decision-makers of power system operation due to its high volatility and uncertainty. One efficient approach to tackling such a problem is using reliable forecasting tools.
Songjian Chai +3 more
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ANN Approach to WECS Power Forecast
2005 IEEE Conference on Emerging Technologies and Factory Automation, 2006In this work-in-progress the problem with the future integration of large quantity of wind generators in the Portuguese electric grid is presented. A method based in artificial neural networks (ANN) is used to predict the average hourly wind speed. The work starts by choosing the patterns set length, the ANN structure and the learning method.
Pedro M. Fonte, José Carlos Quadrado
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The Power Load Forecasting by Kernel PCA
2010We use one year's subset to train the Support Vector Machines (SVM) and the next year's data was used for testing with Kernel Principal Components Analysis (KPCA). This is clearly not optimal for a non-stationary time series such as we have here nevertheless the MAPE of peak load data set with back-propagation neural network [Chuang et al., 1998] is 3 ...
Fang-Tsung Liu +3 more
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