• Energy Research

Abstract This paper presents a system for the real-time analysis of human skin temperatures using sensor fusion and thermal image recognition. The aim of this work is to introduce an open and extensible framework that supports multi-modal sensor input with a focus on merging optical data and conventional sensor input for advanced thermal comfort analysis. The goal is to obtain a more complete representation of a person in various indoor climatic conditions. Methods proposed in this paper are important for research and industrial applications with respect to the real-time analysis of thermal comfort and human physiology in indoor climates. Although this paper mainly focuses on the analysis of skin temperatures, the proposed architecture is conceived for being extendable for statistical evaluation and numerical models. Arbitrary software components can be integrated as data sources and sinks by means of a conventional TCP/IP networking interface. Main contributions of this paper are a general architecture for the fusion of multi-modal sensor input using a centralized data server structure, a method for combining depth-map based face and pose tracking with a thermal imaging device and preliminary studies demonstrating the behavior and validity of the system.

Abstract The aim of this work is to develop and validate a miscellaneous electric loads (MEL) predictive model that does not require occupant-wise or building-wise model training nor model adaptation while achieving competitive accuracy. For that purpose, a long-short-term memory (LSTM) model was developed using monitored data from a research building located in Abu Dhabi, United Arab Emirates (UAE). In order to test the generalization capabilities of the proposed method, the model was evaluated using data from two additional buildings, a bank office building located in Frankfurt, Germany, and a university building in Ottawa, Canada. The results showed that the developed LSTM is applicable to the tested buildings without the need for occupant-wise or building-wise calibration, hence, addressing an important gap in the existing literature. In addition, a set of MEL predictive models from the literature, that are based on a Weibull distribution and Gaussian mixture models (GMM) are implemented and evaluated using the three identical data sets. The round-robin evaluation of existing MEL predictive models showed that the proposed LSTM model outperformed them especially when a combination of MEL and occupancy information was used as inputs. Finally, the neural network saturation was identified as the key challenge when developing an LSTM-based model for MEL prediction.

This study explores the applicability of data augmentation techniques for reconstructing missing energy time -series in limited data regimes. In particular, multiple synthetic copies of a relatively small training dataset are stacked together with pseudo-random noise. First, an existing convolutional denoising autoencoder is selected from a previous work, as the base imputation model of this study. Then, an optimal augmentation rate, which minimizes the training set of the model, is chosen based on the preliminary results obtained from one building. The results proved that, augmenting 80 times a nine days-long training set could reduce the initial average root mean squared error (RMSE) by 37% and 48%, for continuous and random missing scenarios. Additionally, the augmented model outperformed the benchmark methods with 23% and 12% lower average RMSE. No additional tuning or calibration costs were required for the existing base imputation model. Therefore, the presented data augmentation technique could significantly reduce the expensive computational costs associated with deep learning models.

AbstractUsing optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, “Time series aggregation for energy system design: Modeling seasonal storage”, has developed a seasonal storage model to address this issue. Simultaneously, the paper “Optimal design of multi-energy systems with seasonal storage” has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results.

Building and environment 208, 108552 (2021). doi:10.1016/j.buildenv.2021.108552 Published by Elsevier, New York, NY [u.a.]

Urban Building Energy Modelling (UBEM) requires adequate geometrical information to represent buildings in a 3D digital form. However, open data models usually lack essential information, such as building geometries, due to a lower granularity in available data. For heating demand simulations, this scarcity impacts the energy predictions and, thereby, questioning existing simulation workflows. In this paper, the authors present an open-source CityGML LoD Transformation (CityLDT) tool for upscaling or downscaling geometries of 3D spatial CityGML building models. With the current support of LoD0–2, this paper presents the adapted methodology and developed algorithms for transformations. Using the presented tool, the authors transform open CityGML datasets and conduct heating demand simulations in Modelica to validate the geometric processing of transformed building models.

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 ��C, 1.30 % and 78.41 ppm, respectively. Accepted in Building and Environment