Improving occupant comfort through real-time Predictive control of Indoor environment using Predicted-Mean Vote model and MLP

TL;DR

This paper presents a real-time predictive control system for indoor environments using PMV and MLP models, improving HVAC efficiency and occupant comfort through experimental validation.

Contribution

It introduces a novel real-time optimization method combining PMV and MLP models for dynamic indoor comfort prediction and HVAC control.

Findings

Enhanced energy efficiency in HVAC systems.

Maintained occupant comfort levels during environmental fluctuations.

Validated effectiveness through experiments in controlled settings.

Abstract

The Predicted Mean Vote (PMV) index is a widely accepted method in the building automation sector because it can precisely estimate indoor thermal comfort levels depending on a variety of environmental parameters. This study suggests an experimental setup for automated real-time optimization of heating, ventilation and air-conditioning (HVAC) operations in closed spaces utilizing PMV-based modelling and Multilayer perceptron (MLP) based prediction, with an experimental setup which includes ESP32 and BME280 sensor. The main objective of this paper is to employ the MLP algorithm and predicted mean vote model to dynamically predict comfort differences considering the fluctuations in environmental conditions such as temperature and humidity. The proposed method is implemented across various settings, encompassing both an anechoic chamber and laboratory environments. The findings indicate that the proposed approach effectively enhances the management of HVAC systems in confined areas, leading to higher energy efficiency and enhanced indoor thermal comfort. The effectiveness of the suggested solution is supported by experimental validation, which shows that significant energy savings are achieved while maintaining comfort levels for occupants within.