Fuzzy logic energy management for a photovoltaic solar home

Chekired, Fathia; Mahrane, Achour; Samara, Zoubeyr; Chikh, Madjid; Guenounou, Abderrezak; Meflah, Aissa

doi:10.1016/j.egypro.2017.09.566

Cited by 46 publications

(27 citation statements)

References 12 publications

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“…Chekired et al [19] suggested a fuzzy logic energy management system (FLEMS) for a photovoltaic solar home. A process was developed to control the domestic energy demand based primarily on the energy available from the PV system and on a load priority that was tailored to meet the energy needs and comfort of the customer.…”

Section: Literature Survey and Features Of This Research Papermentioning

confidence: 99%

Fuzzy Efficient Energy Smart Home Management System for Renewable Energy Resources

2020

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This article provides a fuzzy expert system for efficient energy smart home management systems (FES-EESHM), demand management, renewable energy management, energy storage, and microgrids. The suggested fuzzy expert framework is utilized to simplify designing smart microgrids with storage systems, renewable sources, and controllable loads on resources. Further, the fuzzy expert framework enhances energy and storage to utilize renewable energy and maximize the microgrid’s financial gain. Moreover, the fuzzy expert system utilizes insolation, electricity price, wind speed, and load energy controllably and unregulated as input variables to enable energy management. It uses input variables including insolation, electrical quality, wind, and the power of uncontrollable and controllable loads to allow energy management. Furthermore, these input data can be calculated, imported, or predicted directly via grid measurement using any prediction process. In this paper, the input variables are fuzzified, a series of rules are specified by the expert system, and the output is de-fuzzified. The findings of the expert program are discussed to explain how to handle microgrid power consumption and production. However, the decisions on energy generated, controllable loads, and own consumption are based on three outputs. The first production is for processing, selling, or consuming the energy produced. The second output is used for controlling the load. The third result shows how to produce for prosumer’s use. The expert method can be checked via the hourly input of variable values. Finally, to confirm the findings, the method suggested is compared to other available approaches.

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Section: Literature Survey and Features Of This Research Papermentioning

confidence: 99%

Fuzzy Efficient Energy Smart Home Management System for Renewable Energy Resources

2020

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“…This house is devoted to be mainly fed by photovoltaic electricity. The house is equipped with a photovoltaic system which consists of a 3.2 kWp multi-crystalline silicon (m-Si) PV generator, on the roof and facing the south with a 20° of tilt angle to the horizontal, a 12 kWh lead-acid battery storage and a 4 kWp gridconnected inverter [5,16]. The purpose of this work is to automate the artificial lighting and secure the house by integrating various sensors in order to command and control them via the microcontrollers.…”

Section: Case Studymentioning

confidence: 99%

“…On the other hand, beside the improvement of house's thermal insulation and efficient consumer appliances, reducing the energy demand also means the introduction of home automation. This is mainly due to the appliances and the various electronic devices used at daily homes [5,6]. Through literature review, different lighting control techniques and technologies were studied [7].…”

Section: Introductionmentioning

confidence: 99%

Low Cost Automation System for Smart Houses based on PIC Microcontrollers

Chekired

Houtti

Bouroussis

et al. 2020

2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Syst

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The high electricity bills of houses have triggered a significant research for solutions to mitigate this issue. The house automation systems offer everything necessary to put an end to these inconveniences as, most often, they incorporate a number of smart devices (microcontrollers and sensors). The presented work outlines the development of a microcontroller-based automation system of a solar smart house using automatic lighting and thermal comfort sensors (temperature, humidity) and safety functions (gas leakage, smoke detection). This system is based on PIC microcontrollers and is applied through the implementation of a complete algorithm. The program is written on Micro C, and in order to realize the control circuit and the simulation, the testing software made use of Proteus software which is freely available from the Internet. The control of temperature and air pollution was implemented using the PIC16F877A, while the light control was applied with the PIC18F4550. The hardware prototype was also provided to experiment on the designed control strategy. The results of this work allowed some conclusions to be reached and confirmed the benefits of this kind of automation systems.

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“…Previous research on photovoltaic systems with fuzzy logic control has been developed with the theme of fuzzy logic on energy management from photovoltaics for solar homes developed by Fathia Chekired (2017) [14]. This research presents a strategy in supplying electricity load from a photovoltaic network system back up, where this system utilizes a Photofoltaik network as a backup energy source, while the main energy source uses a utility grid.…”

Section: Introductionmentioning

confidence: 99%

Smart grid photovoltaic system pilot scale using sunlight intensity and state of charge (SoC) battery based on Mamdani fuzzy logic control

Faqih

Primadi

Handayani

et al. 2019

J. Mechatron. Electr. Power Veh. Technol.

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The Utilization of renewable energy such as a photovoltaic system is the foremost alternative in transfers generated by conventional power plants, but the lack of photovoltaics is support for light intensity. The purpose of this research is to develop a pilot-scale smart grid photovoltaic system that can regulate the supply of electrical energy from either the battery or the power supply. The control system in this study uses the Mamdani fuzzy logic method in determining automatic system performance. This system monitors the intensity of light and battery which are then used as automatic safety parameters on the power supply, battery, and photovoltaic. The results of this study display the indicator results from the microcontroller in supplying electrical energy for the use of electrical loads, Power Supply has been served the load when the battery is in a low state which have a voltage <11 Volts, the battery has been served the load when the condition of the battery is in a medium and high condition which has a voltage of 11.5 <; ....; <13 Volts. PV has been served batteries or loads when the light intensity is cloudy and bright which have a light intensity of 3585 <; ...; <10752 Lux. This system can reduce dependence on conventional energy without reducing the quality of the energy supply at load and Photovoltaic system dependence on light intensity does not affect the supply of energy consumption to electrical loads.

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Fuzzy logic energy management for a photovoltaic solar home

Cited by 46 publications

References 12 publications

Fuzzy Efficient Energy Smart Home Management System for Renewable Energy Resources

Fuzzy Efficient Energy Smart Home Management System for Renewable Energy Resources

Low Cost Automation System for Smart Houses based on PIC Microcontrollers

Smart grid photovoltaic system pilot scale using sunlight intensity and state of charge (SoC) battery based on Mamdani fuzzy logic control

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