Design and Development of Optimal and Deep-Learning-Based Demand Response Technologies for Residential Hybrid Renewable Energy Management System

The principal goal of this study is to conduct a techno-economic analysis of hybrid energy generation designs for residential-form houses in urban areas. Various possibilities for a form house electrification system are created and simulated in order to determine an optimum ideal configuration for m...

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Bibliographic Details
Published in:Sustainability 2023-09, Vol.15 (18), p.13773
Main Authors: Krishnamoorthy, Murugaperumal, Raj, P. Ajay-D-Vimal, Subramaniam, N. P, Sudhakaran, M, Ramasamy, Arulselvi
Format: Article
Language:English
Subjects:
Alternative energy sources
Carbon
Case studies
Design
Efficiency
Electricity distribution
Energy industry
Energy management
Energy management systems
Energy resources
Energy storage
Force and energy
Forecasts and trends
Industrial plant emissions
Learning strategies
Nuclear energy
Nuclear power plants
Optimization
Power supply
Renewable resources
Research methodology
Simulation methods
Supply and demand
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Summary:The principal goal of this study is to conduct a techno-economic analysis of hybrid energy generation designs for residential-form houses in urban areas. Various possibilities for a form house electrification system are created and simulated in order to determine an optimum ideal configuration for meeting residential load demand with an increase in energy capacity and minimal investment. Using NREL’s HOMER optimization tool, a case-study-based virtual HRE model is developed. Pre-assessment data and relevant operation constraints are used to build the system’s objective functions. The instantaneous energy balance algorithm technique is used to solve the multi-objective function. The overall optimization procedure is sandwiched between two supporting advanced approaches, pre- and post-operations. The development of an optimal techno-economic hybrid energy generation system for the smooth fulfillment of urban load demand is aided by novel deep belief network (NDBN)-based pre-stage load demand predictions and an analysis of the necessary demand side management (DSM)practicing code for utility efficiency improvements in post-stage simulations.
ISSN:2071-1050
2071-1050
DOI:10.3390/su151813773