Residential Demand Side Management Using Artificial Intelligence

2021 ◽  
Author(s):  
Ajith Vijayan ◽  
Venugopalan Kurupath ◽  
Jani Das
Keyword(s):  
Artificial Intelligence ◽  
Demand Side Management ◽  
Demand Side ◽  
Residential Demand

scholarly journals Financial and economic feasibility of bio-digesters for rural residential demand-side management and sustainable development

2021 ◽  
Vol 7 ◽  
pp. 1728-1741
Author(s):  
Edson L. Meyer ◽  
Ochuko K. Overen ◽  
KeChrist Obileke ◽  
John J. Botha ◽  
Jan J. Anderson ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Demand Side Management ◽  
Economic Feasibility ◽  
Demand Side ◽  
Residential Demand

scholarly journals Optimal Design and Model Predictive Control of Standalone HRES: A Real Case Study for Residential Demand Side Management

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 29767-29814 ◽  
Author(s):  
Shafiqur Rehman ◽  
Habib Ur Rahman Habib ◽  
Shaorong Wang ◽  
Mahmut Sami Buker ◽  
Luai M. Alhems ◽  
...  
Keyword(s):  
Optimal Design ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Demand Side Management ◽  
Real Case ◽  
Demand Side ◽  
Residential Demand

scholarly journals Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2047 ◽  
Author(s):  
Yung-Yao Chen ◽  
Yu-Hsiu Lin ◽  
Chia-Ching Kung ◽  
Ming-Han Chung ◽  
I-Hsuan Yen
Keyword(s):  
Artificial Intelligence ◽  
Data Science ◽  
Modern Society ◽  
Smart Homes ◽  
Demand Side Management ◽  
Demand Side ◽  
Smart Meters ◽  
Smart Power ◽  
Iot Applications ◽  
User Centric

In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today’s modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable.

scholarly journals Analysis of Cool Roof Coatings for Residential Demand Side Management in Tropical Australia

Energies ◽  
2015 ◽  
Vol 8 (6) ◽  
pp. 5303-5318 ◽  
Author(s):  
Wendy Miller ◽  
Glenn Crompton ◽  
John Bell
Keyword(s):  
Demand Side Management ◽  
Demand Side ◽  
Cool Roof ◽  
Tropical Australia ◽  
Residential Demand
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