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.

Efficient power scheduling in smart homes using a novel artificial ecosystem optimization technique considering two pricing schemes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Souhil Mouassa ◽  
Marcos Tostado-Véliz ◽  
Francisco Jurado
Keyword(s):  
Energy Demand ◽  
Supply And Demand ◽  
Optimization Technique ◽  
Smart Homes ◽  
Demand Side Management ◽  
Time Interval ◽  
Demand Side ◽  
Artificial Ecosystem ◽  
Pricing Schemes ◽  
Operational Time

Abstract With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificial ecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. Artificial ecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff.

scholarly journals An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes

Energies ◽  
2017 ◽  
Vol 10 (9) ◽  
pp. 1258 ◽  
Author(s):  
Awais Manzoor ◽  
Nadeem Javaid ◽  
Ibrar Ullah ◽  
Wadood Abdul ◽  
Ahmad Almogren ◽  
...  
Keyword(s):  
Smart Homes ◽  
Demand Side Management ◽  
Smart Metering ◽  
Demand Side ◽  
Hybrid Heuristic

scholarly journals Blockchain Enabled Distributed Demand Side Management in Community Energy System With Smart Homes

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 37428-37439 ◽  
Author(s):  
Muhammad Afzal ◽  
Qi Huang ◽  
Waqas Amin ◽  
Khalid Umer ◽  
Asif Raza ◽  
...  
Keyword(s):  
Smart Homes ◽  
Demand Side Management ◽  
Energy System ◽  
Demand Side

An Energy Ontology Focusing on Demand Side Management in Smart Homes

2016 ◽  
Vol 19 ◽  
pp. 124-131
Author(s):  
Beate Naser ◽  
Franziska Schäfer ◽  
Jörg Franke
Keyword(s):  
Energy Production ◽  
Power Plants ◽  
Renewable Energy Sources ◽  
Electric Energy ◽  
Smart Homes ◽  
Demand Side Management ◽  
Demand Side ◽  
Conceptual Foundation ◽  
Available Energy ◽  
Solar Power Plants

By increasing the share of renewable energy sources, the volatility of available energy is rising. More and more fluctuating power generation by solar power plants and wind turbines has to be integrated into the power grid. Demand side management (DSM) represents one possible solution to achieve this goal by including energy production and energy consumption simultaneously. In this paper, we especially focus on the field of electric energy in smart homes. Considering the implementation of different DSM devices, an ontology-based approach can serve as a conceptual foundation for a necessary knowledge base. We propose an advanced energy ontology for smart homes, integrating important aspects for a successful DSM. We describe how power producers, storages and consumers are represented in our ontology. Finally, we show the scenario-based utilization of our approach.

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