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

Abstract

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.

AI-Enhanced Power Management System for Buildings: A Review and Suggestions

“…Jalal and Al-Rubayi [32] 2022 ML ANN Benavente and Ibadah [33] 2020 ML SVC, DT, K-Neighbors, Logistic Regression, Gausian NB, Kaligambe et al [34] 2022 ML XGBoost Elbes et al [35] 2019 DL LSTN Afuosi and Zoghi [36] 2020 ML KNN Yang et al [37] 2020 ML NARX ANN, MLP ANN Valladares et al [38] 2019 DL DRL Gao et al [39] 2020 DL DRL, FNN Yu et al [40] 2021 DL DRL Moghadasi et al [41] 2021 ML GBM Diyan et al [42] 2020 DL DRL Lissa et al [43] 2020 DL DRL Zhang et al [44] 2021 Dl DNN Haq et al [45] 2021 DL DRL Shan et al [5] 2019 ML Ensemble learning…”

“…The simulation results revealed that the improved strategy enhances the intelligent building while achieving rational and efficient energy allocation. Jalal and Al-Rubayi [32] presented a study to develop a Smart Building Control System (SBCS) to reduce energy usage in a building by utilizing daylight sensors, occupant control, weather variations, load consumption, and changes in solar power. The energy consumption was reduced by 56% in the summer and 65% in the winter.…”