In the light of global climate change and the current energy crisis, it is crucial to target sustainable energy use in all sectors. Buildings still remain one of the most energy-demanding sectors. Campus buildings and higher educational buildings are important to target due to their high and increasing energy demand. This building segment also represents a research gap, as mostly office or domestic buildings have been studied previously. In the quest for thermal comfort, a key stakeholder in building energy demand is the building occupant. It is therefore crucial to promote energy-aware behaviors. The building systems are another key factor to consider. As conventional building systems are replaced with smart building systems, the entire scenario is redrawn for how building occupants interact with the building and its systems. This study argues that behavior is evolving with the smartness of building systems. By means of a semi-systematic literature review, this study presents key findings from peer-reviewed research that deal with building occupant behavior, building systems and energy use in campus buildings. The literature review was an iterative process based on six predefined research questions. Two key results are presented: a graph of reported energy-saving potentials and a conceptual framework to evaluate building occupants impact on building energy use. Furthermore, based on the identified research gaps in the selected literature, areas for future research are proposed. 

Building Occupants, Their Behavior and the Resulting Impact on Energy Use in Campus Buildings: A Literature Review with Focus on Smart Building Systems

Abstract Quantitatively separating the influence of climate change and human activities on runoff is crucial to achieving sustainable water resource management in watersheds. This study presents a framework for quantitative assessment by integrating the indicators of hydrologic alteration, the whale optimization algorithm and random forest (WOA-RF), and the water erosion prediction (WEP-L) models. This framework quantifies the differences in hydrological conditions and their driving forces at multi-timescales (annual, seasonal, and monthly). The results indicate that the runoff of the Wu River has decreased since 2005. Climate factors were found to influence the interannual variation of runoff mainly. Meanwhile, human activities had a more significant impact in autumn, with a relative contribution rate of 59.0% (WOA-RF model) and 70.8% (WEP-L model). Monthly, the picture is more complex, with the results of the WOA-RF model indicating that climate change has a significant impact in July, August, and September (88.8%, 92.7%, and 79.3%, respectively). However, the WEP-L model results showed that the relative contribution of land use is significant in April, May, June, October, and November (51.24%, 64.23%, 63.63%, 53.16%, and 50.63%, respectively). The results of the study can be helpful for regional water allocation. 

Multi-scale flow regimes and driving forces analysis based on different models: a case study of the Wu river basin

Intentions and actions in household waste separation: A machine learning approach on the gap and determinants

Energy use behavior research: A scientometric evaluation and critical synthesis

Research on the drivers of energy-related behaviors in buildings has gained significant interest in recent years. However, existing studies are often limited to a restricted scope of analysis (e.g., single-domain and single-cultural contexts) and rely on simplistic data analysis tools that may fail to capture complex relationships of the studied drivers of behavior. Based on a survey of university students in Abu Dhabi (UAE, n = 519) and Aachen (GER, n = 153), this study investigates the impact of demographic characteristics, personality traits, energy-related beliefs, and social perceptions on the energy conservation motivation and attempts to save energy. Two distinct modeling approaches are applied and compared: (i) traditional linear regression (often used by social scientists), and (ii) machine learning based random forest regression (often used by data scientists). When using the training and testing set from the same location, the traditional linear regression showed slightly higher predictive power and significantly higher explanatory power. In contrast, the random forest regression was more successful in predicting the participants’ attitude towards energy saving by generalizing the behavior from different locations. In either case, the most influential drivers of occupant behavior were social influence and energy conservation beliefs and ability. The Abu Dhabi and Aachen samples shared the same drivers (i.e., triggers) of behavior despite disclosing different motivation levels and attempts to conserve energy. The findings shed meaningful insights on the multi-domain nature of occupant behavior across different cultures and the premise of different data analysis approaches to capture complex relationships. 

Crossing borders and methods: Comparing individual and social influences on energy saving in the United Arab Emirates and Germany

This paper reports the first results of a comparison of human and algorithmic anomaly detection. We are interested in how human and automated anomaly detection can be combined in the most beneficial way to improve how fault detection is practiced in building maintenance. Open source datasets with sensor data were annotated by persons with low subject matter experience, and compared with a Convolutional Autoencoder Neural Network (CAE) as well as a dedicated time series algorithm (DeepAnT), and detection metrics of human and algorithmic procedures are compared. Future comparisons will include higher levels of expertise on the human side, and more sophistication/training amount on the algorithm side. We close by discussing the advantages and caveats of our approach.

Comparing human and algorithmic anomaly detection for HVAC systems applications

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Crossing borders and methods: Comparing individual and social influences on energy saving in the United Arab Emirates and Germany

Comparing human and algorithmic anomaly detection for HVAC systems applications