A gap-filling method for room temperature data based on autoencoder neural networks

This study explores the applicability of a deep learning-based approach for reconstructing missing room temperature data from different domains where relatively few training samples are available. For that purpose, the existing convolutional, long short-term memory (LSTM) and feed-forward autoencoders were combined with a suitable domain adaptation procedure. Eventually, the developed models were evaluated on data collected in four buildings with significant differences in thermal mass, design and location. The findings pointed out that the domain adaptation can be conducted effciently by using a small data sample from the target domain. Additionally, the results showed that the proposed model can reconstruct up to 80 % of the missing daily room temperature inputs with RMSE accuracy of 0.6 °C

Indoor environment data time-series reconstruction using autoencoder neural networks

As the number of installed meters in buildings increases, there is a growing number of data time-series that could be used to develop data-driven models to support and optimize building operation. However, building data sets are often characterized by errors and missing values, which are considered, by the recent research, among the main limiting factors on the performance of the proposed models. Motivated by the need to address the problem of missing data in building operation, this work presents a data-driven approach to fill these gaps. In this study, three different autoencoder neural networks are trained to reconstruct missing short-term indoor environment data time-series in a data set collected in an office building in Aachen, Germany. This consisted of a four year-long monitoring campaign in and between the years 2014 and 2017, of 84 different rooms. The models are applicable for different time-series obtained from room automation, such as indoor air temperature, relative humidity and $CO_{2}$ data streams. The results prove that the proposed methods outperform classic numerical approaches and they result in reconstructing the corresponding variables with average RMSEs of 0.42 {\deg}C, 1.30 % and 78.41 ppm, respectively.Comment: Accepted in Building and Environmen

Generic occupant behavior modeling for commercial buildings

Human-building interactions are driven by a complex combination of social, psychological and physiological factors. As such, occupants’ energy consumption related actions can not be addressed using analytical approaches, as conventionally adopted in building performance simulation (BPS) and building automation systems (BASs). An additional degree of occupant behavior (OB) modeling complexity comes from occupants’ individuality and diversity. As a consequence, occupants’ energy consumption related behavior may be highly variant, even in case of similar settings and indoor environmental quality (IEQ). Given a large occupant population, this result in complex, yet contradictory requirements on diversity representation and model scalability. The aim of this thesis is to develop models towards more reliable OB modeling. In particular, it is aimed to gain knowledge towards more generic OB modeling, that could be applicable for a number of diverse occupants in different commercial building settings. For that purpose, the methodological focus is on machine learning (ML) methods using physical monitoring data. In order to obtain and quantify models’ generalization to alternative occupants’ and buildings, the building-wise modeling paradigm is followed through the major part of this thesis. Firstly, the potential of the time-independent OB in terms of manual window openings is explored. The modeling is conducted using conventional machine learning and deep learning classification approaches. The data imbalance is identified as a key modeling challenge. The obtained results show that the random forest based classification and developed deep learning model can reliably represent window opening behavior in given settings. As an alternative to the time-independent modeling, the sequence based modeling of OB is explored. The modeling objectives is defined to be adaptive and non-adaptive OB in commercial settings. The resulting target functions are window states modeling and miscellaneous electric loads (MELs). The sequential nature of proposed models is represented by including the time-series of past IEQ and OB measurements as the model inputs. The results show that the model formulations where the short- and long-term past of IEQ and OB data are used as inputs resulted in improved models’ performance, when compared to the alternative, established methods. Conclusively, the imbalanced properties of OB data and limited models’ applicability to alternative buildings are identified as the major limitations of the current OB modeling practices, that are addressed in the scope of this thesis. Finally, the presented models lead to more accurate yet scalable OB modeling and they show the practical potential for the inclusion in BAS and BPS as end-use real-life applications

Non-Intrusive Data Monitoring and Analysis of Occupant Energy-Use Behaviors in Shared Office Spaces

A non-intrusive data collection framework is developed to analyze the desk-level occupancy and energy use patterns of occupants in shared office spaces. The framework addresses the limitations of previous studies in the literature, which either lacked the granularity to study individual occupants' behaviors or relied on data from complex Building Management Systems (BMS). The framework is applied to a shared office space of an academic institution in the United Arab Emirates (UAE), where occupancy, lighting, and plug-load data were collected from individual desks for 6 months. The results highlight weak relationships between the occupancy status and the total electric loads, with 35% of the total electric loads consumed when the area is completely vacant, and 64% of the plug-load energy consumed when the desks were reported as unoccupied. While specific to the studied building, the results highlight the role that a high-resolution data monitoring framework plays in capturing inefficient consumption patterns. The findings also confirm the contribution of occupant behavior (OB) to the energy performance gap commonly observed between predicted and actual energy levels