http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Methods, Best Practices, Case Studies, and Applications
Demand response (DR) describes controlled changes in the power consumption whose role is to better match the power demand with the supply. This reference, written by an international team of experts from academia and industry, covers the principles, implementation and applications of DR.
Description based upon print version of record.
Chapter 1: A comprehensive review on industrial demand response strategies and applicationsChapter 2: Demand response cybersecurity for power systems with high renewable power shareChapter 3: Recurrent neural networks for electrical load forecasting to use in demand responseChapter 4: Optimal demand response strategy of an industrial customerChapter 5: Price-based demand response for thermostatically controlled loadsChapter 6: Electric vehicle massive resources mining and demand response applicationChapter 7: Demand response measurement and verification approaches: analyses and guidelinesChapter 8: Transactive energy industry demand response management marketChapter 9: Industrial demand response opportunities with residential appliances in smart gridsChapter 10: Modelling and optimal scheduling of flexibility in energy-intensive industryChapter 11: Industrial demand response: coordination with asset managementChapter 12: A machine learning-based approach for industrial demand responseChapter 13: Feasibility assessment of industrial demand responseChapter 14: Measurement and verification of demand response: the customer load baselineChapter 15: Modeling and optimizing the value of flexible industrial processes in the UK electricity marketChapter 16: Case study of Aran Islands: optimal demand response control of heat pumps and appliancesChapter 17: Use case of artificial intelligence, and neural networks in energy consumption markets, and industrial demand response.
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Stevenage : Institution of Engineering & Technology,c2022