A short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling
Author:
Affiliation:
1. College of Electrical Engineering Shanghai University of Electric Power Shanghai People's Republic of China
2. College of Computer Science and Technology Shanghai University of Electric Power Shanghai People's Republic of China
Funder
National Natural Science Foundation of China
Program of Shanghai Academic Research Leader
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/gtd2.12409
Reference41 articles.
1. KhatoonS.&SinghA.K.et al.: Analysis and comparison of various methods available for load forecasting: An overview. In: 2014 Innovative Applications of Computational Intelligence on Power Energy and Controls with their impact on Humanity (CIPECH). Ghaziabad India pp. 243–247 (2014)
2. Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest
3. DehalwarV. KalamA. KolheM.L. ZayeghA.:Electricity load forecasting for urban area using weather forecast information. In:2016 IEEE International Conference on Power and Renewable Energy (ICPRE).Shanghai China pp.355–359(2016)
4. Probabilistic load forecasting via quantile regression averaging on sister forecasts;Liu B.;IEEE Trans. Smart Grid,2015
5. Conventional models and artificial intelligence-based models for energy consumption forecasting: A review
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