Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification

Luo

et al. 2020

Energy and Buildings

Self Cite

234

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

Section: Machine Learning For Building Controlmentioning

confidence: 99%

State-of-the-art on research and applications of machine learning in the building life cycle

Luo

et al. 2020

Energy and Buildings

Self Cite

234

“…Buildings consume a large proportion of energy and emit a substantial amount of greenhouse gas to maintain a comfortable thermal environment [1], [2] for occupants' comfort, satisfaction [3], productivity [4], health [5] and well-being. The approaches to curtail building energy consumption while improving environmental quality not only include applying energy efficient technologies [6] and materials [7], enhancing building sensing, prediction [8], [9] and control [10], but also rely on a better understanding of occupants' behaviors and their true demands. A key research question to understand occupants' thermal demand is what is the suitable indoor temperature set-point that could satisfy occupants' comfort need at affordable energy consumption level [11].…”

Section: Introductionmentioning

confidence: 99%

Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

Renewable and Sustainable Energy Reviews

2020

Self Cite

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants' comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4°C for the cooling mode with a median of 23.7°C, and between 20.5 and 24.9°C for the heating mode with a median of 22.7°C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2-3°C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking.

“…With HVAC (heating, ventilation, air-conditioning) systems consuming over 40% of building energy use, improving efficient HVAC control is a key issue in building energy saving studies [1,2] Under building operation phase, not only can occupants interact with building to maintain indoor thermal comfort and environment quality, also occupants can passively participate in building load transfer, therefore, the influence of occupancy on buildings' performance increases [3,4]. Occupancy detection and prediction are inspiring researches for efficient HVAC controls and developing building energy efficiency models [5,6]. Previously, occupancy was usually estimated with a single parameter with single sensor, e.g.…”

Section: Introductionmentioning

confidence: 99%

Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering

Building and Environment

et al. 2019

Self Cite

Fusing various sensing data sources is able to improve the accuracy and reliability of building occupancy detection. Efficiently fusing environmental sensors and wireless network signals is seldom studied for its computational and technical challenges. This study aims to propose an integrated model that is able to extract critical data features for environmental and Wi-Fi probe dual sensing sources to promote computational efficiency. The adaptive lasso model was introduced for the feature extraction and reduction process. To validate the proposed model, an onsite experiment was conducted and two occupancy resolutions, real-time and four-level occupancy resolutions, were compared. The results suggested that eight features among all twelve features are most relevant. The mean absolute error of the selected data features is about 2.18 for real-time occupancy and F1_accuracy is about 84.36% for four-level occupancy.