Abstract
The outdoor air temperature affects the building’s interior temperature. It can be noted that the outdoor weather patterns were found repetitive in nature, and historical records of outdoor temperature are readily accessible. A tendency to forecast the climatically data and make decisions on temperature control was exhibited by the previous models; but, they failed to consider building thermal learning. Both climatically data and building learning data are taken as input by the proposed Reinforcement Learning-based Restricted Boltzmann Machine (RL-RBM) based Bellman-Zadeh Fuzzy–Programmable Logic Controller (BZF-PLC) controller. The SSK clustering algorithm is utilized to preprocess the input parameters and cluster them regarding temperature. Then, clustered outcomes were assessed, and these are the considered features to train RL-RBM for future prediction. Then, in the BZF-PLC controller, predicted outcomes are provided that make decisions, and then, hot water storage in the passive heat/cooling system is activated. Solar Air Heater (SAH) and Absorber chiller are encompassed in this system. Overall, the proposed model was found to be more outstanding when it was evaluated and analogized with prevailing temperature control techniques.
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