Abstract

There are two primary ways to save energy within a building: (1) through improving building engineering structures and adopting efficient appliance ownership, and (2) through changing occupants’ energy-consuming behaviors. Unfortunately the second way suffers from many challenges and limitations. Occupant behavior is, indeed, a complex and multi-disciplinary concept depending on several human factors. Although its importance is recognized by the energy management community, it is often oversimplified and naively defined when used to study, analyze or model energy load. This paper aims at promoting the definition of occupant behavior as well as exploring the extent to which the latter is involved in research works, targeting directly or indirectly energy savings. Hence, in this work, we propose an overview of interdisciplinary research approaches that consider occupants’ energy-saving behaviors, while we present the big picture and evaluate how occupant behavior is defined, we also propose a categorization of the major works that consider energy-consuming occupant behavior. Our findings via a literature review methodology, based on a bibliometric study, reveal a growth of the number of research works involving occupant behavior to model load forecasting and household segmentation. We have equally identified a research trend showing an increasing interest in studying how to successfully change occupant behaviors towards energy saving.

Highlights

  • Examples of keywords include prediction, forecast, prognosis, classification, machine learning categorize, pattern and synonyms along those lines. These results correspond to papers (i.e., 485) that focus on forecast and prediction of occupant behavior, on load forecasting while considering OB, and on comfort-related studies

  • This study has reviewed research works on energy saving that involve occupant behaviors and household characteristics in the residential sector, while underlining the importance of OB for energy saving, we have identified three major aims for considering

  • We urge the development of a comprehensive definition of OB to avoid oversimplification, complexity and omission of multidisciplinary aspect of the user interactions with energy devices

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Summary

Introduction with regard to jurisdictional claims in

The proportion of energy consumption from the building sector constitutes a large part of the overall energy consumption around the world, accounting for almost 40% of total electricity consumption within the European Union (EU) [1]. Some researchers advocate that prospective occupants should be actively involved during the design and operation of buildings to better understand and consider occupants’ behavior towards energy-use efficiency [16]. For these reasons, promoting the rigor of studies on households behavior and their impact on energy saving is of a high importance towards clean and affordable energy.

Research Methodology and Bibliometric Findings
Objective
Occupant Behaviors Receive an Increasing Interest in the Recent Years
Research Works on Occupant Behaviors are Majorly Led by Developed Economies
Applied Definitions of Occupant Behavior
Theoretical Frameworks of Occupant Behavior
Occupant Behavior Data
Load Profile and Load Signature
OB Meta-Data
Major Categories of Occupant-Behaviors Related Works
Improving Energy Consumption Forecasting
Improvements Based on Occupancy
Improvement Based on Load Profiles
Improvement Based on Predicting Appliance-Use Patterns
Summary of Load Forecasting Works Considering OBs
Households Segmentation
Clustering of Household Energy-Use Behavior
Classification of Household and Building Characteristics
Determination of Appliance Load Profile in Buildings
Changing Occupant Behaviors Towards Energy-Saving
Changing Appliances Use-Behaviors
Strategies to Change Occupant Behaviors
Increasing the Level of Awareness and Commitment to Change
Lessons Learned
Challenges and Limitations
Opportunities and Trends
Alleviating Data Complexity and Households Variability
Analyzing Load Profile and Household Characteristics Extraction
Clustering OBs as a Prior Step for Better-Performing Building Load Prediction
Potential for Future Research
Findings
Conclusions
Full Text
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