A multidisciplinary research approach to energy-related behavior in buildings

Occupant behavior in buildings is one of the key drivers of building energy performance. Closing the “performance gap” in the building sector requires a deeper understanding and consideration of the “human factor” in energy usage. For Europe and US to meet their challenging 2020 and 2050 energy and GHG reduction goals, we need to harness the potential savings of human behavior in buildings, in addition to deployment of energy efficient technologies and energy policies for buildings. Through involvement in international projects such as IEA ECBC Annex 53 and EBC Annex 66, the research conducted in the context of this thesis provided significant contributions to understand occupants’ interactions with building systems and to reduce their energy use in residential and commercial buildings over the entire building life cycle. The primary goal of this Ph.D. study is to explore and highlight the human factor in energy use as a fundamental aspect influencing the energy performance of buildings and maximizing energy efficiency – to the same extent as technological innovation. Scientific literature was reviewed to understand state-of-the-art gaps and limitations of research in the field. Human energy-related behavior in buildings emerges a stochastic and highly complex problem, which cannot be solved by one discipline alone. Typically, a technological-social dichotomy pertains to the human factor in reducing energy use in buildings. Progressing past that, this research integrates occupant behavior in a multidisciplinary approach that combines insights from the technical, analytical and social dimension. This is achieved by combining building physics (occupant behavior simulation in building energy models to quantify impact on building performance) and data science (data mining, analytics, modeling and profiling of behavioral patterns in buildings) with behavioral theories (engaging occupants and motivating energy-saving occupant behaviors) to provide multidisciplinary, innovative insights on human-centered energy efficiency in buildings. The systematic interconnection of these three dimensions is adopted at different scales. The building system is observed at the residential and commercial level. Data is gathered, then analyzed, modeled, standardized and simulated from the zone to the building level, up to the district scale. Concerning occupant behavior, this research focuses on individual, group and collective actions. Various stakeholders can benefit from this Ph.D. dissertation results. Audience of the research includes energy modelers, architects, HVAC engineers, operators, owners, policymakers, building technology vendors, as well as simulation program designers, implementers and evaluators. The connection between these different levels, research foci and targeted audience is not linear among the three observed systems. Rather, the multidisciplinary research approach to energy-related behavior in buildings proposed by this Ph.D. study has been adopted to explore solutions that could overcome the limitations and shortcomings in the state-of-the-art research

Data Mining of Occupant Behavior in Office Buildings

Literature studies confirm occupant behavior is setting the direction for contemporary researches aiming to bridge the gap between predicted and actual energy performance of sustainable buildings. Using the Knowledge Discovery in Database (KDD) methodology, two data mining learning processes are proposed to extrapolate office occupancy and windows’ operation behavioral patterns from a two-years data set of 16 offices in a natural ventilated office building. Clustering procedures, decision tree models and rule induction algorithms are employed to obtain association rules segmenting the building occupants into working user profiles, which can be further implemented as occupant behavior advanced-inputs into building energy simulations

A Data-mining Approach to Discover Patterns of Window Opening and Closing Behavior in Offices:

Understanding the relationship between occupant behaviors and building energy consumption is one of the most effective ways to bridge the gap between predicted and actual energy consumption in buildings. However effective methodologies to remove the impact of other variables on building energy consumption and isolate the leverage of the human factor precisely are still poorly investigated. Moreover, the effectiveness of statistical and data mining approaches in finding meaningful correlations in data is largely undiscussed in literature. This study develops a framework combining statistical analysis with two data-mining techniques, cluster analysis and association rules mining, to identify valid window operational patterns in measured data. Analyses are performed on a data set with measured indoor and outdoor physical parameters and human interaction with operable windows in 16 offices. Logistic regression was first used to identify factors influencing window opening and closing behavior. Clustering procedures were employed to obtain distinct behavioral patterns, including motivational, opening duration, interactivity and window position patterns. Finally the clustered patterns constituted a base for association rules segmenting the window opening behaviors into two archetypal office user profiles for which different natural ventilation strategies as well as robust building design recommendations that may be appropriate. Moreover, discerned working user profiles represent more accurate input to building energy modeling programs, to investigate the impacts of typical window opening behavior scenarios on energy use, thermal comfort and productivity in office buildings

Occupancy schedules learning process through a data mining framework:

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10 minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. The identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings

A Data-mining Approach to Discover Patterns of Window Opening and Closing Behavior in Offices:

Understanding the relationship between occupant behaviors and building energy consumption is one of the most effective ways to bridge the gap between predicted and actual energy consumption in buildings. However effective methodologies to remove the impact of other variables on building energy consumption and isolate the leverage of the human factor precisely are still poorly investigated. Moreover, the effectiveness of statistical and data mining approaches in finding meaningful correlations in data is largely undiscussed in literature. This study develops a framework combining statistical analysis with two data-mining techniques, cluster analysis and association rules mining, to identify valid window operational patterns in measured data. Analyses are performed on a data set with measured indoor and outdoor physical parameters and human interaction with operable windows in 16 offices. Logistic regression was first used to identify factors influencing window opening and closing behavior. Clustering procedures were employed to obtain distinct behavioral patterns, including motivational, opening duration, interactivity and window position patterns. Finally the clustered patterns constituted a base for association rules segmenting the window opening behaviors into two archetypal office user profiles for which different natural ventilation strategies as well as robust building design recommendations that may be appropriate. Moreover, discerned working user profiles represent more accurate input to building energy modeling programs, to investigate the impacts of typical window opening behavior scenarios on energy use, thermal comfort and productivity in office buildings

Smart meters and energy savings in Italy: Determining the effectiveness of persuasive communication in dwellings

To secure a sustainable energy development in the residential sector, attitudes and human behavior need to be modified toward more efficient and conscious energy usage. The goal of this research is to assess evaluations and to test the effectiveness in reducing domestic electricity consumption. The aim of the smart monitoring system we evaluate is to provide households with a user-friendly tool that improves awareness of energy behavior in homes, enabling better management via the visualization of consumption and persuasive tailored information on domestic electricity use. In our study, the system was tested on 31 Italian families selected among volunteers all over Italy, participating to the first trial phase from October 2012 to November 2013. A combination of persuasive communication strategies such as graphical real-time and historical feedback based on real data and comparison tools to encourage competitiveness against “similar” households were provided to users through a domestic user-friendly interface. In addition, personalized energy saving prompts were sent via web-newsletters to trial users. The study concludes that energy related persuasive communication is effective in reducing electricity consumption in dwellings on average −18% and up to −57%

Occupant Behavior of Window Opening and Closing in Office Buildings: Data Mining Approaches

Occupant behavior is stochastic, complex, and multi-disciplinary. Studies have shown significant impact of occupant behavior on energy use and environmental performance of both residential and commercial buildings. The understanding of the relationship between occupant behavior and building energy consumption can be seen as one of the most effective ways to bridge the gap between predicted and actual energy consumption in buildings. However effective methodologies to remove the effects of other variables on building energy consumption and isolate the leverage of the human factor precisely are still poor investigated. Moreover, the use of data mining approaches in finding meaningful correlations in a large data set is rarely discussed in existing literature. In a view of these facts, this study employs two data mining methods, cluster analysis and association rules, to discover patterns of windows opening and closing in a dataset with: 10-minute interval data over two complete years, 16 offices of a natural ventilated building, and a dozen measured indoor and outdoor physical parameters. The windows opening/closing patterns consider diversity and presence of occupants, time of day and day of year, and important indoor and outdoor environmental parameters. The proposed data mining approaches can be used to disaggregate occupant behavior into clusters and to categorize typical drivers of behavior in office buildings. Final goal is to identify valid, novel, potential useful and understandable patterns of occupant behavior into measured building data. The identified windows opening/closing patterns will be represented as typical occupant profiles that can be used as input to current building energy modelling programs, like EnergyPlus and IDA-ICE, to investigate impact of windows opening and closing behavior on energy use and design of natural ventilation in building