Demand Response Potential Day-Ahead Forecasting Approach Based on LSSA-BPNN Considering the Electricity-Carbon Coupling Incentive Effects

In order to address the challenges of global climate change and to achieve the goals of sustainable development, the need for a low-carbon transition of the power system is becoming more urgent. However, previous demand response (DR) potential forecasting models for load aggregators (LAs) only consi...

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Veröffentlicht in:	IEEE transactions on industry applications Jg. 60; H. 3; S. 4505 - 4516
Hauptverfasser:	Zhang, Yiran, Ge, Xinxin, Li, Meiyi, Li, Nan, Wang, Fei, Wang, Liyong, Sun, Qinfei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.05.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Back propagation networks Biological system modeling Carbon Carbon dioxide Carbon emissions Climate change Customers Data models Day-ahead forecasting Demand response demand response potential Economic impact Electric potential Electric power demand Electric power systems Electricity electricity-carbon coupling Electrode potentials Emissions Energy management Forecasting Home automation load aggregator Load modeling low-carbon Low-carbon economy Neural networks Power consumption Power system planning Redundancy Residential energy Search algorithms Sustainable development
ISSN:	0093-9994, 1939-9367
Online-Zugang:	Volltext
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