Energy Optimization Techniques for Demand-Side Management in Smart Homes

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.

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A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

Information ◽

10.3390/info10030108 ◽

2019 ◽

Vol 10 (3) ◽

pp. 108 ◽

Cited By ~ 14

Author(s):

Abdul Shah ◽

Haidawati Nasir ◽

Muhammad Fayaz ◽

Adidah Lajis ◽

Asadullah Shah

Keyword(s):

Energy Consumption ◽

Residential Buildings ◽

Optimization Algorithms ◽

Energy Optimization ◽

Smart Homes ◽

Electrical Energy ◽

Optimization Techniques ◽

Comfort Level ◽

Energy Consumption Optimization ◽

User Comfort

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

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

Energies ◽

10.3390/en10091258 ◽

2017 ◽

Vol 10 (9) ◽

pp. 1258 ◽

Cited By ~ 26

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

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

IEEE Access ◽

10.1109/access.2020.2975233 ◽

2020 ◽

Vol 8 ◽

pp. 37428-37439 ◽

Cited By ~ 17

Author(s):

Muhammad Afzal ◽

Qi Huang ◽

Waqas Amin ◽

Khalid Umer ◽

Asif Raza ◽

...

Keyword(s):

Smart Homes ◽

Demand Side Management ◽

Energy System ◽

Demand Side

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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 ◽

10.3390/s19092047 ◽

2019 ◽

Vol 19 (9) ◽

pp. 2047 ◽

Cited By ~ 17

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.

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An Energy Ontology Focusing on Demand Side Management in Smart Homes

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.19.124 ◽

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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