Short-term load forecasting using machine learning and periodicity decomposition-Reference-Cited by-同舟云学术

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Short-term load forecasting using machine learning and periodicity decomposition

Published:2019 Issue:3 Volume:7 Page:382-394
ISSN:2333-8334
Container-title:AIMS Energy
language:en
Short-container-title:

Author:

El khantach Abdelkarim, ,Hamlich Mohamed,eddine Belbounaguia Nour,

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment

Reference32 articles.

1. Review of demand response under smart grid paradigm

2. Demand response in electrical energy supply: An optimal real time pricing approach;Faria P, Vale Z;Energy

3. A reliability perspective of the smart grid;Moslehi K, Kumar R;IEEE Trans Smart Grid

4. Load/price forecasting and managing demand response for smart grids: Methodologies and challenges;Chan SC, Tsui KM, Wu HC, et al.;IEEE Signal Process Mag

5. Book: Time series analysis

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2. An efficient load forecasting technique by using Holt‐Winters and Prophet algorithms to mitigate the impact on power consumption in COVID‐19;IET Energy Systems Integration;2024-01-02

3. Estimation of Power Consumption Prediction of Electricity Using Machine Learning;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024

4. Development of Machine Learning Based Daily Peak Load Forecasting System for Winter Season in the State of Meghalaya in India;Lecture Notes in Electrical Engineering;2023-11-01

5. Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast;Energies;2023-10-07

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