Abstract
This study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure HVAC is not run needlessly when when a room/zone is unoccupied. In this paper, we propose simple yet effective algorithms to predict occupancy alongside an algorithm for automatically assigning temperature set-points. Utilizing past occupancy observations, we introduce three different techniques for occupancy prediction. Firstly, we propose an identification-based approach, which identifies the model via Expectation Maximization (EM) algorithm. Secondly, we study a novel finite state automata (FSA) which can be reconstructed by a general systems problem solver (GSPS). Thirdly, we introduce an alternative stochastic model based on uncertain basis functions. The results show that all the proposed occupancy prediction techniques could achieve around 70% accuracy. Then, we have proposed a scheme to adaptively adjust the temperature set-points according to a novel temperature set algorithm with customers’ different discomfort tolerance indexes. By cooperating with the temperature set algorithm, our occupancy-based HVAC control shows 20% energy saving while still maintaining building comfort requirements.
Highlights
Accurate and reliable occupancy detection is becoming a key enabler for demand-response HVAC control, which requires the capturing of occupancy changes in real time [1]
We propose three different occupancy prediction methods for demand-based HVAC
All three proposed short-term stochastic modeling methods, general systems problem solver (GSPS), Expectation Maximization (EM) and uncertain basis, achieved more than 70% accuracy in the experimental studies
Summary
More than 30% of building energy is consumed by HVAC systems, which usually operate on a fixed schedule predefined by building owners or operation managers. As a promising remedy for the aforementioned issue, can achieve significant energy savings by temporally matching the building energy consumption and building usage This has the potential to reduce up to a third of HVAC energy consumption. The energy consumption of that building is dominated by the occupancy and related activities [7] It follows that there exists optimal control parameters, based on the instantaneous number of humans in a building and their associated behaviors, with great energy savings potential. Occupancy in a building is stochastic both in time and space, which greatly affects actual power consumption for an individual zone or building This will affect our decisions for improving energy efficiency and in implementing the advanced demand response (Typically peak-shaving applications in modern energy management systems [8,9]). Occupant behavior is well recognized as a dominant source of the discrepency between predicted and actual building performance, and developing accurate short-term occupancy prediction will greatly enhance implementation of realistic building energy modeling and control
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