Electrical Energy Management Based on a Hybrid Artificial Neural Network-Particle Swarm Optimization-Integrated Two-Stage Non-Intrusive Load Monitoring Process in Smart Homes

Lin, Yu‐Hsiu; Hu, Yu‐Chen

doi:10.3390/pr6120236

Cited by 32 publications

(38 citation statements)

References 23 publications

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“…In [17] and [18] the authors used intelligent techniques for power optimization in a hybrid vehicle. Also, in [19,20], the authors' work was based on fuzzy techniques for managing online the power inside the vehicle.…”

Section: The Ev Power Management Problem In the Literaturementioning

confidence: 99%

“…The principle is related to energetic optimization. Supervising and controlling the electrical power inside cities can be a benefit for managing the generation part [19]. Researchers have used the expression "smart" due to the possibility of exchanging information between houses and buildings [1,47].…”

Section: Proposed Power Management Approach For An Ev In Relation To mentioning

confidence: 99%

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A Novel Energy Optimization Approach for Electrical Vehicles in a Smart City

Aymen

Mahmoudi

2019

Energies

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Electric Vehicles (EVs) have emerged rapidly across the globe as a powerful eco-friendly initiative that if integrated well with an urban environment could be iconic for the city’ host’s commitment to sustainable mobility and be a key ingredient of the smart city concept. This paper examines ways that will help us to develop a better understanding of how EVs can achieve energy use optimization and be connected with a smart city. As a whole, the present study is based on an original idea that would be useful in informing policy-makers, automotive manufacturers and transport operators of how to improve and embrace better EV technologies in the context of smart cities. The proposed approach is based on vehicles and buildings communication for sharing some special information related to the vehicle status and to the road condition. EVs can share their own information related to the energy experience on a specific path. This information can be gathered in a gigantic database and used for managing the power inside these vehicles. In this field, this paper exposes a new approach to power management inside an electric vehicle based on bi-communication between vehicles and buildings. The principle of this method is established on two sections; the first one is related to vehicles’ classification and the second one is attached to the buildings’ recommendation, according to the car position. The classification problem is resolved using the support vector classification method. The recommendation phase is resolved using the artificial intelligence principle and the neural network was employed, for giving the best decision. The optimal decision will be calculated inside the building, according to its position and using the old vehicle’s data, and transferred to the coming vehicle, for optimizing its energy consumption method in the corresponding building zone. Different possibilities and situations were discussed in this approach. The proposed power management methodology was tested and validated using Simulink/Matlab tool. Results related to the battery state of charge and to the consumed energy were compared at the end of this work, for showing the efficiency of this approach.

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Section: The Ev Power Management Problem In the Literaturementioning

confidence: 99%

Section: Proposed Power Management Approach For An Ev In Relation To mentioning

confidence: 99%

A Novel Energy Optimization Approach for Electrical Vehicles in a Smart City

Aymen

Mahmoudi

2019

Energies

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“…In Flah 16 and Geetha and Subramani, 17 authors used intelligent techniques for power optimizing in a hybrid vehicle. The online power management problem was taken in the previous studies [18][19][20] in which authors used fuzzy techniques for treating this problem. Other solution using the vehicle GPS position, the driver's control method, and the vehicle sensors outputs was present for managing power inside the vehicle.…”

Section: A Literature Review On the Power Management Problem Insidementioning

confidence: 99%

Design and analysis of a novel power management approach, applied on a connected vehicle as V2V, V2B/I, and V2N

Aymen

Mahmoudi

2019

Int J Energy Res

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Summary The communication technologies were evolving rapidly and making everything connected. Peoples, vehicles, and buildings become connected everywhere. Concentrating on the targets that vehicle can be connected with, the present paper exposes a new power management approach applicable for three kinds of connected vehicles. Vehicle to buildings or infrastructure, vehicle to network, and vehicle to vehicle are the three communication protocols between vehicle and external targets. The proposed approach is designed for implementing all of these three communication cases. It is based on the principle of vehicle data sharing. Each vehicle will share a package of data, which contain information related to the vehicle status and the road condition. This information will be collected in a database and used for managing the power inside these cars. This approach is built on two stages; the first one is related to vehicles' classification, and the second one is attached to the recommendation, and this is according to the car position and according to the communication protocol case. The first stage was resolved using the support vector classification method. The second stage was treated using the artificial intelligence principle, and the neural network was employed. So the optimal decision will be outputted. According to the vehicle communication method, the optimal decision will be identified inside the building, infrastructure, vehicle, or the cloud database. This will be related to the vehicle position using the old vehicle's data. So the obtained decission will be transferred to the coming vehicle, for optimizing its energy consumption method in the corresponding area. Different possibilities and situations were discussed in this approach. The power management methodology was verified and confirmed. For showing the efficiency of this approach, a comparison of the obtained results in relation to the battery state of charge and the consumed energy results will be done. Matlab simulink was used for doing the necessary tests.

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“…Since different types of appliances have different opportunities and ways to participate in DR, it is crucial to study detailed appliance-level power consumption. In addition, the visualization of detailed consumption of high-power appliances will help customers to replace some inefficient devices, so as to save energy [2].…”

Section: Introductionmentioning

confidence: 99%

“…It is a good idea to complete NILM with unsupervised learning by combining different features [4]. The most commonly used steady-state signatures are active and reactive power [2,5]. They are effective in identifying high-power devices, but it is challenging to separate low-power appliances for them due to the possibility of power overlap.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

Gao

et al. 2019

Applied Sciences

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Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks.

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Electrical Energy Management Based on a Hybrid Artificial Neural Network-Particle Swarm Optimization-Integrated Two-Stage Non-Intrusive Load Monitoring Process in Smart Homes

Cited by 32 publications

References 23 publications

A Novel Energy Optimization Approach for Electrical Vehicles in a Smart City

A Novel Energy Optimization Approach for Electrical Vehicles in a Smart City

Design and analysis of a novel power management approach, applied on a connected vehicle as V2V, V2B/I, and V2N

Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

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