Results 261 to 270 of about 8,068,929 (298)

Robust short-term load forecasting

International Workshop on Systems, Signal Processing and their Applications, WOSSPA, 2011
Analyzing the stochastic characteristics of electric consumption series in many countries shows the presence of atypical observations or outliers. Outliers are deviant data points that do not follow the model of the majority of observations. They significantly degrade the accuracy of conventional day-ahead estimation and forecasting methods even if ...
Yacine Chakhchoukh, Abdelhak M. Zoubir
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Fuzzy interaction regression for short term load forecasting [PDF]

Fuzzy Optimization and Decision Making, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tao Hong 0003, Pu Wang
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Short Term Load Forecasting Based on Weather Load Models

IFAC Proceedings Volumes, 1987
Abstract A stochastic weather load model for on-line forecasting of hourly load demands with lead times of 1 to 168 hours is presented. The proposed model considers the effect of up to 3 weather variables. The multi-input (hourly weather variables) and single output (hourly loads) stochastic process is modeled as an Auto Regressive-Moving Average ...
S. Vemuri, B. Hoveida, S. Mohebbi
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Studies on the forecasting errors of the short term load forecast

Proceedings. International Conference on Power System Technology, 2003
A deep research on the short-term load forecasting error has been given in this paper according to the time series theory. The relationship between the model and forecasting error has been investigated. A method proposed could be used to direct how to improve the forecasting accuracy. Instances of different methods test its feasibility.
G. Mu, Y.H. Chen, null Liu ZF, W.D. Fan
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Short-term load forecasting using a long short-term memory network

2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2017
Load forecasting is an essential part of a power system. It enhances the energy-efficiency and reliable operation of the power system. As depicted in the proposal of the smart grid, an increasing number of smart meters have been being installed in many utilities on a global scale.
Chang Liu, Zhijian Jin, Jie Gu 0005, Robert Caiming Qiu   +3 more
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Long short term memory networks for short-term electric load forecasting

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017
Short-term electricity demand forecasting is critical to utility companies. It plays a key role in the operation of power industry. It becomes all the more important and critical with increasing penetration of renewable energy sources. Short-term load forecasting enables power companies to make informed business decisions in real-time.
Apurva Narayan, Keith W. Hipel
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Local Regression-Based Short-Term Load Forecasting

Journal of Intelligent and Robotic Systems, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Neighbor histories for short term electrical load forecasting

2001 European Control Conference (ECC), 2001
This paper shows the application of the Neighbor Histories (NH) algorithm to the problem of short term electrical load forecasting in a utility company. This algorithm is a simple application of embedding theorems recently used in chaotic time series prediction.
Manuel R. Arahal, Eduardo F. Camacho
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Short-Term Load Demand Modeling and Forecasting: A Review

IEEE Transactions on Systems, Man, and Cybernetics, 1982
Both the off-line and on-line methods for short-term electric load forecasting are reviewed. Since identifying an adequate model is the most important problem of any forecasting technique, the literature is classified according to the modeling approaches used for representing the load demand.
Mohamed A. Abu-El-Magd, Naresh K. Sinha
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Short-Term Load Forecasting based on ResNet and LSTM

2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2018
Recent development of artificial intelligence (AI) makes AI applicable to diverse fields, and the smart grid is not an exception. In particular, there have been extensive researches on load forecasting using deep learning. Most existing studies have been conducted on deep neural network (DNN) and recurrent neural network (RNN).
Hyungeun Choi, Seunghyoung Ryu, Hongseok Kim   +2 more
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