Adaptive HVAC System Based on Fuzzy Controller Approach

Abstract

Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort. Hence, this study proposes a novel fuzzy controller with additional input parameters to keep indoor thermal comfort consistent with the corresponding number of occupants. The process employs an automatic fuzzy rule generation method to simplify the task of generating rules in the fuzzy inference system (FIS) using Mamdani FIS. A design-builder is used for modeling the HVAC systems. Local weather data were used to conduct simulations via EnergyPlus. The thermal comfort analysis using the Fanger model for three different scenarios shows that the proposed FIS controller can successfully respond to the indoor comfort variation in all possible scenarios and ensure a satisfactory comfort level. The proposed method demonstrates up to 50% energy savings if occupants do not worry about comfort.

Multimodal Framework for Smart Building Occupancy Detection

“…The IoT thermostat collects the occupancy number, sets the desired set point, and instructs the controller to maintain the desired set point to ensure that thermal comfort is kept within pre-agreed comfort bounds (see Figure 7). Our initial study in [29] proposed a novel adaptive controller for an HVAC system or IoT thermostat that can predict preferred temperature boundaries based on known environmental parameters (such as the level of indoor CO 2 , temperature, and humidity). Every five minutes, the controller requests the current indoor temperature and occupancy with the IoT thermostat to compare the current indoor temperature with the preferred temperature and decide whether to adjust the HVAC set point temperature or not (see Figure 7).…”

“…Technologies Technique Accuracy (%) [2] Camera and sensors Machine Learning 89-99 [29] Sensors Machine Learning 79-85 [14] Camera and sensors Machine Learning 76-99 Proposed approach Camera and sensors Machine Learning 89-99.6…”

“…Therefore, this study explored and analyzed current approaches for smart building occupancy prediction to improve the quality of occupancy-related datasets that are used for training purposes through the use of a multimodal data fusion approach within the smart home ecosystem. Previous research employed interactive learning to label occupant numbers during data collection [2,14,28,29]. However, the proposed data fusion approach uses a parametric classifier to ensure a strong correlation between the predictor (room occupancy) and predicting parameters (occupancy-related data in the room), verify the quality of occupancy-related data, and filter noisy measurements from sensors without being overly invasive using rule-based decisions.…”

“…However, the proposed data fusion approach uses a parametric classifier to ensure a strong correlation between the predictor (room occupancy) and predicting parameters (occupancy-related data in the room), verify the quality of occupancy-related data, and filter noisy measurements from sensors without being overly invasive using rule-based decisions. In addition, a lighting control parameter is incorporated together with HVAC system control, unlike current studies [29,30]. A random forest was used to handle the occupancy prediction tasks (other ML algorithms could be integrated) to balance HVAC energy usage with thermal comfort.…”

An Adaptive Control Model for Thermal Environmental Factors to Supplement the Sustainability of a Small-Sized Factory

“…The fuzzy system has provided an effective solution in ambiguous decision models that include difficult problems to solve in the conventional ways or rules. In particular, it presents a more effective solution in a model in which a particular situation, such as indoor thermal comfort, is difficult to define as a single value or definition (i.e., good, quite good, or very good) [4,5]. As large amounts of data are continuously accumulated, models that analyze existing data trends and predict results have been widely used in addition to models that execute decision making when specific values are input.…”