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Short-term load forecasting in an electric power system

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Short-term load forecasting: CEMIG power industry's approach

1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 2002
Several methods exist to establish relations between variables, the most common of which is the construction of models. These models enable the expression of physical relationships or laws between variables. Empirical experimentation, measurement and expert knowledge are alternate means to obtain data and establish said rules.
G. Lambert-Torres, A.P. Alves da Silva, C.I.A. Costa, A.F. Cardoso, W. Marra   +4 more
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Short-term load forecasting: A power-regression approach

2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2014
The short-term load forecasting problem is addressed by means of a power regression approach. Exploiting the highly correlated nature of the explanatory variables, just two loads are deemed sufficiently informative for prediction purposes: one day before and one week before.
G. De Nicolao, M. Pozzi, E. Soda, M. Stori   +3 more
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Attention-Bidirectional LSTM Based Short Term Power Load Forecasting

2021 Power System and Green Energy Conference (PSGEC), 2021
Power load forecasting plays an important role in the development of power system, which can provide crucial guidance for power supply. The short term power load forecasting can ensure the safety and stability of power grids in a short time. To solve the problem of insufficient accuracy of prediction, Attention-Bidirectional LSTM based short term power
Zhangliang Wang, Li Jia, Chang Ren
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A methodology for Short-term Electric Power Load Forecasting

2019 9th International Conference on Advances in Computing and Communication (ICACC), 2019
Energy consumption has been increasing steadily due to globalization and industrialization. As a result electricity load forecasting has gained vital importance in order to conserve energy and other resources. But due to the uncertain characteristics of forecasting methods, it is still one among the most difficult task to get implemented with accurate ...
Smithu Izudheen, Anu Maria Joykutty
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Holographic Ensemble Forecasting Method for Short-Term Power Load

IEEE Transactions on Smart Grid, 2019
In this paper, we newly propose a holographic ensemble forecasting method (HEFM). First, we use the mutual information and statistical method to select feature variables, which is an ensemble of information about the cross-border multi-source data at the dataset level.
Mo Zhou, Min Jin
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Short Term Power Load Forecasting by Using Neural Models

2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC), 2009
This paper presents the power load forecasting by using neural models for Toronto area, Canada. Different neural models were used to carry out the forecasting works. One-day-ahead daily total load and peak load forecasts were implemented by using different neural models in order to find the more accurate forecasting results.
Huang-Chi Chen, Yi-Ching Lin, Yu-Ju Chen, Chuo-Yean Chang, Huang-Chu Huang, Rey-Chue Hwang   +5 more
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Short-Term Power Load Forecast in Electric Companies

Advanced Materials Research, 2014
This paper studies the problem of load forecast in electric companies. We combine the analysis of load cause and gray prediction model together, and enhance the accuracy of prediction, thus improving the economic benefit of electric companies and saving energy resources.
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Power Short-Term Load Forecasting Based on QPSO-SVM

Advanced Materials Research, 2012
The values of parameters of support vector machine have close contact with its forecast accuracy. In order to accurately forecast power short-term load,we presented a power short-term load forecasting method based on quantum-behaved particle swarm optimization and support vector machine.First,cauchy distribution was used to improve the quantum particle
Xing Tong Zhu, Bo Xu
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Short Term Power Load Forecasting Based on Clustering and Long Short Term Memory Network

2021 IEEE 4th International Electrical and Energy Conference (CIEEC), 2021
To improve the accuracy and reliability of load forecasting, this paper proposes a load forecasting method based on clustering and LSTM(long short term memory network). Firstly, the K-means clustering method is used to learn the regional power consumption model.
WanTing Zheng, QingZhu Wan, Liu Zhu, Ning Lv, Mingyang Li   +4 more
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