Results 241 to 250 of about 215,326 (298)

Spatial-Temporal Residential Short-Term Load Forecasting via Graph Neural Networks

IEEE Transactions on Smart Grid, 2021
Electric load forecasting, especially short-term load forecasting, is of significant importance for the safe and efficient operation of power grids. With the wide adoption of advanced smart meters, more attention has been paid to short-term residential ...
Weixuan Lin, Di Wu, B. Boulet
semanticscholar   +1 more source

A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid Using MLR and LSTM Neural Network

IEEE Transactions on Industrial Informatics, 2021
The short-term load forecasting is crucial in the power system operation and control. However, due to its nonstationary and complicated random features, an accurate forecast of the load behavior is challenging.
Jian Li, Daiyu Deng, Junbo Zhao, Dongsheng Cai, Weihao Hu, Man Zhang, Qi Huang   +6 more
semanticscholar   +1 more source

Short-term load forecasting based on load profiling

2013 IEEE Power & Energy Society General Meeting, 2013
Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility
Ramos, Sérgio, Soares, João, Vale, Zita, Ramos, Sandra   +3 more
openaire   +2 more sources

Hybrid Multitask Multi-Information Fusion Deep Learning for Household Short-Term Load Forecasting

IEEE Transactions on Smart Grid, 2021
With the detailed load data provided by smart meter, the learning of electricity usage behavior for individual household short-term load forecasting has become a hot research topic.
Lianjie Jiang, Xinli Wang, Wei Li, Lei Wang, Xiaohong Yin, Lei Jia   +5 more
semanticscholar   +1 more source

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
openaire   +1 more source

Standardization of short-term load forecasting models

2012 9th International Conference on the European Energy Market, 2012
There has been a significant production of load forecasting models over the last 5 years. These models present a wide variety of techniques, most of them using novel artificial intelligence approaches. Load forecasting is a complex matter and it is the result of several processes that, depending on the database, may be of more or less importance ...
López García, Miguel, VALERO, SERGIO, Senabre, Carolina, Gabaldon, Antonio, Aparicio, Juan   +4 more
openaire   +2 more sources

Adaptive Weather-Sensitive Short Term Load Forecast

IEEE Transactions on Power Systems, 1987
This paper introduces an adaptive, weather sensitive, short term load forecast algorithm that has been developed for two South Carolina Power Systems: CEPCI (Central Electric Power Cooperatives, Inc., Central for short) and Combined System. The model is based on a State Space formulation specially tailored for this application.
R. Campo, P. Ruiz
openaire   +1 more source

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
openaire   +1 more source

Short-term Load Forecasting of Distribution Transformer Supply Zones Based on Federated Model-Agnostic Meta Learning

IEEE Transactions on Power Systems
With the increasing data privacy concerns raised by not only organizations but also individuals in distribution systems, traditional centralized data-driven forecasting approaches for short-term load forecasting (STLF) in distribution transformer supply ...
Changsen Feng, Liang Shao, Jiaying Wang, Youbing Zhang, Fushuan Wen   +4 more
semanticscholar   +1 more source

A Hybrid Short-Term Load Forecasting Approach for Individual Residential Customer

IEEE Transactions on Power Delivery, 2023
This article proposes a hybrid method (HM) to improve the accuracy of short-term individual residential load forecasting. The HM includes an ensemble model (EM), deep ensemble model (DEM), and thermal dynamic model expressed by resistance-capacitance (RC)
Xin Lin, R. Zamora, C. Baguley, Amal Srivastava   +3 more
semanticscholar   +1 more source