The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

Bissey, Sébastien; Jacques, Sébastien; Bunetel, Jean-Charles Le

doi:10.3390/en10111701

Cited by 34 publications

(38 citation statements)

References 38 publications

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“…The combustion and production of coal, oil and natural gas release harmful toxins into the environment [9]. Toxins emitted into the environment from fossil fuels have intensified renewable energy production and consumption [10]. Yet, securing the environment from emissions has come to the forefront of contemporary issues for most economies around the world [11].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Impact of GDP on CO2 Emissions and Forecasting Africa’s Total CO2 Emissions with Non-Assumption Driven Bidirectional Long Short-Term Memory

Ameyaw

Yao

2018

Sustainability

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The amount of total carbon dioxide (CO2) emissions emitted into the environment threatens both human and global ecosystems. Based on this background, this study first analyzed the relationship between gross domestic product (GDP) and CO2 emissions in five West African countries covering the period of 2007–2014 based on a panel data model. Our causality analysis revealed that there exists a unidirectional causality running from GDP to CO2 emissions. Second, after establishing the nexus between GDP and CO2 emissions, we forecast Africa’s CO2 emissions with the aim of projecting future consumption levels. With the quest to achieve climate change targets, realistic and high accuracy total CO2 emissions projections are key to drawing and implementing realizable environmentally-friendly energy policies. Therefore, we propose a non-assumption driven forecasting technique for long-term total CO2 emissions. We implement our bidirectional long short-term memory (BiLSTM) sequential algorithm formulation for both the testing stage (2006–2014) and forecasting stage (2015–2020) on Africa’s aggregated data as well as the five selected West African countries employed herein. We then propose policy recommendations based on the direction of causality between CO2 emissions and GDP, and our CO2 emissions projections in order to guide policymakers to implement realistic and sustainable policy targets for West Africa and Africa as a whole.

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Section: Introductionmentioning

confidence: 99%

Analyzing the Impact of GDP on CO2 Emissions and Forecasting Africa’s Total CO2 Emissions with Non-Assumption Driven Bidirectional Long Short-Term Memory

Ameyaw

Yao

2018

Sustainability

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“…Hence, in this paper, both the instantaneous power consumption and the power deviation are used to determine a proper QoE threshold. Since the fuzzy theory has been well applied to several different applications [16][17][18], this paper proposes a fuzzy logic control method to determine a proper QoE threshold for the proposed smart appliance control algorithm in Figure 3.…”

Section: Fuzzy-controlled Qoe Thresholdmentioning

confidence: 99%

“…Therefore, how to determine a proper QoE threshold for the smart appliance control algorithm to perform well under various scenarios of different power demands becomes a challenging issue. It is well known that fuzzy logics have been popularly used in an HEMS [16][17][18]. Thus, one aim of this paper is to design a fuzzy logic controller to dynamically adjust the QoE threshold for the proposed smart appliance control algorithm performing well under different profiles of power demands.…”

Section: Introductionmentioning

confidence: 99%

QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles

Chen

et al. 2018

Energies

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To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user’s QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user’s QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user’s QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user’s QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user’s QoE.

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“…HEMS based on fuzzy logic controllers (FLCs) (FLC-based HEMS) were proposed in Refs. [13][14][15][16][17][18][19][20][21][22]. The works are summarized in Table 1, where the input and output variables of the FLC, the objective function, and the implementation platform are presented.…”

Section: Introductionmentioning

confidence: 99%

Embedded Fuzzy Logic Controller and Wireless Communication for Home Energy Management Systems

2018

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Energy management systems in residential areas have attracted the attention of many researchers along the deployment of smart grids, smart cities, and smart homes. This paper presents the implementation of a Home Energy Management System (HEMS) based on the fuzzy logic controller. The objective of the proposed HEMS is to minimize electricity cost by managing the energy from the photovoltaic (PV) to supply home appliances in the grid-connected PV-battery system. A fuzzy logic controller is implemented on a low-cost embedded system to achieve the objective. The fuzzy logic controller is developed by the distributed approach where each home appliance has its own fuzzy logic controller. An automatic tuning of the fuzzy membership functions using the Genetic Algorithm is developed to improve performance. To exchange data between the controllers, wireless communication based on WiFi technology is adopted. The proposed configuration provides a simple effective technology that can be implemented in residential homes. The experimental results show that the proposed system achieves a fast processing time on a ten-second basis, which is fast enough for HEMS implementation. When tested under four different scenarios, the proposed fuzzy logic controller yields an average cost reduction of 10.933% compared to the system without a fuzzy logic controller. Furthermore, by tuning the fuzzy membership functions using the genetic algorithm, the average cost reduction increases to 12.493%.

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The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

Cited by 34 publications

References 38 publications

Analyzing the Impact of GDP on CO2 Emissions and Forecasting Africa’s Total CO2 Emissions with Non-Assumption Driven Bidirectional Long Short-Term Memory

Analyzing the Impact of GDP on CO2 Emissions and Forecasting Africa’s Total CO2 Emissions with Non-Assumption Driven Bidirectional Long Short-Term Memory

QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles

Embedded Fuzzy Logic Controller and Wireless Communication for Home Energy Management Systems

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