Industrial Demand Response : 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.

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Bibliographic Details
Main Author: Alhelou, Hassan Haes.
Other Authors: Moreno-Muñoz, Antonio., Siano, Pierluigi.
Format: eBook
Language: English
Published: Stevenage : Institution of Engineering & Technology, 2022.
Series: Energy Engineering.
Subjects:
ISBN: 1523153482
9781523153480
1523146745
9781523146741
1839535628
9781839535628
Physical Description: 1 online resource (426 pages).

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Table of contents

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100 1 |a Alhelou, Hassan Haes. 
245 1 0 |a Industrial Demand Response :  |b Methods, Best Practices, Case Studies, and Applications. 
264 1 |a Stevenage :  |b Institution of Engineering & Technology,  |c 2022. 
264 4 |c ©2022. 
300 |a 1 online resource (426 pages). 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Energy Engineering 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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. 
500 |a Description based upon print version of record. 
500 |a 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. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Electric power consumption  |x Forecasting. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Moreno-Muñoz, Antonio. 
700 1 |a Siano, Pierluigi. 
776 0 8 |i Print version:  |a Alhelou, Hassan Haes  |t Industrial Demand Response  |d Stevenage : Institution of Engineering & Technology,c2022  |z 9781839535611 
830 0 |a Energy Engineering. 
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