Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings
Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy compet...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/12/2812 |
| _version_ | 1798006146092498944 |
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| author | Chinmaya Mahapatra Akshaya Kumar Moharana Victor C. M. Leung |
| author_facet | Chinmaya Mahapatra Akshaya Kumar Moharana Victor C. M. Leung |
| author_sort | Chinmaya Mahapatra |
| collection | DOAJ |
| description | Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q-learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q-learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand. |
| first_indexed | 2024-04-11T12:50:22Z |
| format | Article |
| id | doaj.art-0f9afd8c838649b7bea262b4c5a2499f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:50:22Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0f9afd8c838649b7bea262b4c5a2499f2022-12-22T04:23:13ZengMDPI AGSensors1424-82202017-12-011712281210.3390/s17122812s17122812Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy SavingsChinmaya Mahapatra0Akshaya Kumar Moharana1Victor C. M. Leung2Department of Electrical and Computer Engineering, The University of British Columbia (UBC), 2332 Main Mall, Vancouver, BC V6T 1Z4, CanadaPower Systems Studies, Powertech Labs Inc., Surrey, BC V3W 7R7, CanadaDepartment of Electrical and Computer Engineering, The University of British Columbia (UBC), 2332 Main Mall, Vancouver, BC V6T 1Z4, CanadaAround the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q-learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q-learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand.https://www.mdpi.com/1424-8220/17/12/2812information and communication technologiessmart citiessmart homehome energy managementQ-learning, user conveniencepeak demandcarbon footprint |
| spellingShingle | Chinmaya Mahapatra Akshaya Kumar Moharana Victor C. M. Leung Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings Sensors information and communication technologies smart cities smart home home energy management Q-learning, user convenience peak demand carbon footprint |
| title | Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings |
| title_full | Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings |
| title_fullStr | Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings |
| title_full_unstemmed | Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings |
| title_short | Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings |
| title_sort | energy management in smart cities based on internet of things peak demand reduction and energy savings |
| topic | information and communication technologies smart cities smart home home energy management Q-learning, user convenience peak demand carbon footprint |
| url | https://www.mdpi.com/1424-8220/17/12/2812 |
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