Energy, economic and environmental analysis of fuzzy logic  controllers used in smart buildings

Baniyounes, Ali M.; Ghadi, Yazeed Yasin; Zahia, Maryam Mahmoud Akho; Adwan, Eyad; Oliemat, Kalid

doi:10.11591/ijpeds.v12.i2.pp1283-1292

Cited by 5 publications

(13 citation statements)

References 16 publications

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“…Fuzzy Logic-based Occupancy Detection (FLOD) is an algorithm for detecting occupancy in a room using sensors and providing real-time feedback to the user [61,62]. This algorithm has been used in many applications, such as home automation and energy management.…”

Section: Fuzzy Logic-based Occupancy Detection (Flod) Algorithmmentioning

confidence: 99%

Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT Sensors

Chaudhari,

Xiao,

Cheng

et al. 2024

Sensors

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Smart buildings use advanced technologies to automate building functions. One important function is occupancy detection using Internet of Things (IoT) sensors for smart buildings. Occupancy information is useful information to reduce energy consumption by automating building functions such as lighting, heating, ventilation, and air conditioning systems. The information is useful to improve indoor air quality by ensuring that ventilation systems are used only when and where they are needed. Additionally, it is useful to enhance building security by detecting unusual or unexpected occupancy levels and triggering appropriate responses, such as alarms or alerts. Occupancy information is useful for many other applications, such as emergency response, plug load energy management, point-of-interest identification, etc. However, the accuracy of occupancy detection is limited by factors such as real-time occupancy data, sensor placement, privacy concerns, and the presence of pets or objects that can interfere with sensor reading. With the rapid development of IoT sensor technologies and the increasing need for smart building solutions, there is a growing interest in occupancy detection techniques. There is a need to provide a comprehensive survey of these technologies. Although there are some exciting survey papers, they all have limited scopes with different focuses. Therefore, this paper provides a comprehensive overview of the current state-of-the-art occupancy detection methods (including both traditional algorithms and machine learning algorithms) and devices with their advantages and limitations. It surveys and compares fundamental technologies (such as sensors, algorithms, etc.) for smart buildings. Furthermore, the survey provides insights and discussions, which can help researchers, practitioners, and stakeholders develop more effective occupancy detection solutions for smart buildings.

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Section: Fuzzy Logic-based Occupancy Detection (Flod) Algorithmmentioning

confidence: 99%

Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT Sensors

Chaudhari,

Xiao,

Cheng

et al. 2024

Sensors

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“…Total power need to generated (KW) AC Nominal power for pv panel (W) DC (20) Total voltage for PV array:…”

Section: Photovoltaic Systemmentioning

confidence: 99%

“…The results of an energy consumption analysis for a commercial building with an integrated photovoltaic (BIPV) facade system are presented in [18]. Researchers developed improved control and automation for buildings and their equipment in order to achieve low energy usage in sector buildings while ignoring the load consumed and the PV input to the controllers from solar systems [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Energy management strategy with smart building control system to reduction electrical load using ANN

Jalal

Al-Rubayi

2022

Bulletin EEI

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Buildings have long been large energy consumers, and inadequate control of heating, ventilation, and air conditioning kinds of variable refrigeration flow (HVAC-VRF) and lighting systems. To reduce energy consumption by using a smart building control system (SBCS) in a building was created using occupant control, daylight sensors, weather condition variations, load consumed, and changes in solar power. The model was tested using MATLAB/Simulink, and it was then utilized to investigate the impact of an integrated system on energy usage based on two scenarios. The first scenario was tested in a simulation of building occupant behavior, meteorological variables, daylight sensors, temperature, and load control. This resulted in energy savings for the HVAC system (23% on summer days and 16% on winter days), and lighting system energy savings (22% on summer days and 15% on winter days). In the second scenario, the building was tested to integrate PV system power with load consumption by using the artificial neural network (ANN) algorithm to manage building load consumption by PV, grid, and diesel generator. As a result, the energy savings were 56% on a summer day and 65% on a winter day of the combined energy utilized by the HVAC and lights.

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“…A different hybrid energy system has been studied in many research papers [6][7][8][9][10][11][12][13][14][15][16][17][18]. The authors in [19] are proposing to realize a technical-economic capacity of a hybrid renewable energy system (HRES) to occupy the energy demand of a university site in the Himalayan state of northeastern Sikkim, and also the other aspect of exploring the electrical voltage of other renewable energy resources such as biogas and syngas and hydrokinetic energy, in addition to a solar-wind hybridization mainly carried out in the resource-rich urban planning territory of the east of the Himalayas.…”

Section: Introductionmentioning

confidence: 99%

“…The panels are equipped with MPPT regulators [9] and connected in parallel to obtain an overall power of 2 kW. This choice was pointed due to their quality/price [9,10]. The connection of the solar panels in parallel mode to multiply the output current and maintain a standardized output voltage at 24 V DC which is fed directly into the common DC bus of the system Considering the case study, the instantaneous power cannot exceed, according to the established dimensioning and for the case of maximum of energy demand, the value of 660W.…”

Section: Introductionmentioning

confidence: 99%

A hybrid renewable energy production system using a smart controller based on fuzzy logic

Moussa

Derrouazin

Latroch

et al. 2022

Electrical Engineering & Electromechanics

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Introduction. This article proposes an improved energy management and optimization system with an intelligent economic strategy based on fuzzy logic technology with multiple inputs and outputs (I/O). It is used to control hybrid electric energy sources built around photovoltaic solar panels, wind turbine and electric energy storage system assisted by the electric grid. The novelty in this work that solar photovoltaic, wind turbine and storage system energy sources are prioritized over the grid network which is solicited only during adverse weather conditions, in order to supply a typical household using up to 4,000 Wh per day. In addition of that, the surplus of renewable energy produced during favorable climatic condition is used to produce hydrogen suitable for household heating and cooking using eletrolyzer system. Purpose. Development of improved energy management and optimization system with an intelligent economic strategy based on fuzzy logic technology. This system is embedded on Arduino 2560 mega microcontroller, on which the fundamental program of fuzzy logic and the distribution of events with all possible scenarios have been implemented according to a flowchart allowing the management of the hybrid system. Methods as well as a parametric search and a simulation to characterize the system, are carried out in order to put on the proposed techniques to ensure continuous accommodation at home. Results. The proposed system results confirm their effectiveness by visualizing the output control signals from the electronic switches. Practical value of which transmits power through a single-phase DC/AC converter to power the AC load for the accommodation.

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Energy, economic and environmental analysis of fuzzy logic controllers used in smart buildings

Cited by 5 publications

References 16 publications

Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT Sensors

Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT Sensors

Energy management strategy with smart building control system to reduction electrical load using ANN

A hybrid renewable energy production system using a smart controller based on fuzzy logic

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