Author
Adam Brun
Bio: Adam Brun is an academic researcher from Aarhus Municipality. The author has contributed to research in topics: Heating system & Thermal energy storage. The author has an hindex of 7, co-authored 8 publications receiving 185 citations.
Papers
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TL;DR: In this paper, a simple autoregressive forecast model with weather prediction input is used to showcase the new concept, which is useful in both the production planning and the online operation of a modern district heating system, in particular in light of the low temperature operation, integration of renewable energy and close interaction with the electricity markets.
83 citations
TL;DR: In this article, a bottom-up model of large groups of residential buildings using data from public building registers, weather measurements, and hourly smart-meter consumption data is presented, based on describing district heating consumption using a modified version of the building energy model described in ISO 13790 in combination with a model of the domestic hot water consumption.
63 citations
TL;DR: In this paper, three different machine learning models are benchmarked for forecasting the aggregated heat load of the large district heating system of Aarhus, Denmark, and the best forecasting performance is achieved with a support vector regression on weather, calendar, and holiday data, yielding a mean absolute percentage error of 6.4% on the 15-38 h horizon.
Abstract: The heat load in district heating systems is affected by the weather and by human behavior, and special consumption patterns are observed around holidays. This study employs a top-down approach to heat load forecasting using meteorological data and new untraditional data types such as school holidays. Three different machine learning models are benchmarked for forecasting the aggregated heat load of the large district heating system of Aarhus, Denmark. The models are trained on six years of measured hourly heat load data and a blind year of test data is withheld until the final testing of the forecasting capabilities of the models. In this final test, weather forecasts from the Danish Meteorological Institute are used to measure the performance of the heat load forecasts under realistic operational conditions. We demonstrate models with forecasting performance that can match state-of-the-art commercial software and explore the benefit of including local holiday data to improve forecasting accuracy. The best forecasting performance is achieved with a support vector regression on weather, calendar, and holiday data, yielding a mean absolute percentage error of 6.4% on the 15–38 h horizon. On average, the forecasts could be improved slightly by including local holiday data. On holidays, this performance improvement was more significant.
51 citations
TL;DR: Examining district heat smart meter data from the largest district heat supplier in Denmark and autocorrelation is identified in the data, it is shown that the K-Means yield acceptable clustering results for district heat data when clustering normalized data, and inclusion of autcorrelation improves the clustering.
27 citations
TL;DR: In this paper, the robustness of future cost-optimal district heating production systems under changing electricity prices, fuel cost and investment cost is estimated through extensive multivariate sensitivity analysis, and the optimal heat production system is characterized in three different electricity pricing scenarios: historical, wind power dominated and demand dominated.
14 citations
Cited by
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
01 Dec 2015
TL;DR: In this article, a review of emerging simulation methods and implementation workflows for bottom-up urban building energy models (UBEM) is presented, as well as an outlook for future developments.
Abstract: Over the past decades, detailed individual building energy models (BEM) on the one side and regional and country-level building stock models on the other side have become established modes of analysis for building designers and energy policy makers, respectively. More recently, these two toolsets have begun to merge into hybrid methods that are meant to analyze the energy performance of neighborhoods, i.e. several dozens to thousands of buildings. This paper reviews emerging simulation methods and implementation workflows for such bottom-up urban building energy models (UBEM). Simulation input organization, thermal model generation and execution, as well as result validation, are discussed successively and an outlook for future developments is presented.
410 citations
01 Apr 2020
TL;DR: A comprehensive literature review of the applications of data mining technologies in this domain and suggestions for future researches are proposed towards effective and efficient data mining solutions for building energy systems.
Abstract: With the advent of the era of big data, buildings have become not only energy-intensive but also data-intensive. Data mining technologies have been widely utilized to release the values of massive amounts of building operation data with an aim of improving the operation performance of building energy systems. This paper aims at making a comprehensive literature review of the applications of data mining technologies in this domain. In general, data mining technologies can be classified into two categories, i.e., supervised data mining technologies and unsupervised data mining technologies. In this field, supervised data mining technologies are usually utilized for building energy load prediction and fault detection/diagnosis. And unsupervised data mining technologies are usually utilized for building operation pattern identification and fault detection/diagnosis. Comprehensive discussions are made about the strengths and shortcomings of the data mining-based methods. Based on this review, suggestions for future researches are proposed towards effective and efficient data mining solutions for building energy systems.
157 citations
TL;DR: Using operation data of real buildings, the performance of different deep learning techniques in automatically deriving high-quality features for building energy predictions is investigated to automate and improve the predictive modeling process while bridging the knowledge gaps between deep learning and building professionals.
154 citations
TL;DR: A strategy for obtaining the thermal response time of building, which is used as the time ahead of prediction models, is proposed and the optimal number of hidden layer nodes is 11 for the extreme learning machine model with feature set 4.
137 citations