Application of supervised learning methods to better predict building energy performance

Building energy consumption is shaped by a variety of factors which prompts a challenge of accurately predicting the building energy performance. Research findings disclosed a significant gap between the building’s predicted and actual energy performance. One of the key factors behind this gap is the occupant’s behavior during operation which includes a set of dependent and independent parameters generating a greater level of uncertainties. To accurately estimate the energy performance, we need to quantify the impact of any observed parameters and further detect its correlation with other parameters. Human behaviors are complex and quantifying the impact of all its interconnected parameters can be error prone and costly. To minimize the performance gap, more scalable and accurate prediction approaches, such as supervised machine learning methods, should be considered. This paper is devoted to investigate the most commonly used supervised learning methods which, when intertwined with conventional building energy performance prediction model, could potentially provide more accurate and reliable estimates. The paper will pinpoint the best use of each studied method in the relation to energy prediction in general and occupant’s behavior in specific and how it can be implemented to better predict building energy performance

Exploration of the Bayesian Network framework for modelling window control behaviour

© 2017 Elsevier Ltd Extended literature reviews confirm that the accurate evaluation of energy-related occupant behaviour is a key factor for bridging the gap between predicted and actual energy performance of buildings. One of the key energy-related human behaviours is window control behaviour that has been modelled by different probabilistic modelling approaches. In recent years, Bayesian Networks (BNs) have become a popular representation based on graphical models for modelling stochastic processes with consideration of uncertainty in various fields, from computational biology to complex engineering problems. This study investigates the potential applicability of BNs to capture underlying complicated relationships between various influencing factors and energy-related behavioural actions of occupants in buildings: in particular, window opening/closing behaviour of occupants in residential buildings is investigated. This study addresses five key research questions related to modelling window control behaviour: (A) variable selection for identifying key drivers impacting window control behaviour, (B) correlations between key variables for structuring a statistical model, (C) target definition for finding the most suitable target variable, (D) BN model with capabilities to treat mixed data, and (E) validation of a stochastic BN model. A case study on the basis of measured data in one residential apartment located in Copenhagen, Denmark provides key findings associated with the five research questions through the modelling process of developing the BN model

追溯技术在旅游者移动行为研究的综述

旅游活动常被认为是日常生活的溢出,而"移动范式"的出现促使旅游活动被置于社会活动的核心位置,移动性也逐渐成为旅游研究中的核心命题。然而,旅游者移动行为研究需要采集大量游客时空移动数据,这在很长一段时间里制约了该研究的进展。近年来,移动互联网技术和地理信息技术的发展,使得对游客个体时空移动信息的精确追溯和记录成为可能,从而给旅游者移动行为研究提供了前所未有的契机。该文全面回顾追溯技术在旅游者移动行为的应用研究,在进行详细文献分析的基础上指出:(1)传统追溯技术和现代追溯技术都有各自优势和劣势,因此将二者有机结合有利于更好地开展旅游者移动行为的研究;(2)每种现代追溯技术均有其各自的优劣势和适用空间尺度,因此需要根据所研究的问题及其空间尺度选择合适的追溯技术;(3)追溯技术的发展在研究尺度、研究对象和研究精度等维度都对旅游者移动行为研究产生了深远的影响。国家自然科学基金项目“基于轨迹数据的景区游客时空运动模式挖掘及内在机理研究”(71601164);;教育部人文社会科学基金项目“大数据驱动的景区游客时空运动模式挖掘与分析研究”(16YJC630177);;福建省中青年教师教育科研项目“移动互联网时代下的游客时空行为模式研究”(JAS160017);;中央高校基本科研业务费专项资金“移动互联网时代下的旅游景区游客时空运动模式研究”(20720161005)资助~

Impact of Occupant Behaviour (OB) on building energy use and thermal comfort: From stochastic modelling and occupant profiling to interdisciplinary user engagement

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Prediction of Indoor Movements Using Bayesian Networks

Abstract. This paper investigates the efficiency of in-door next location prediction by comparing several prediction methods. The scenario concerns people in an office building visiting offices in a regular fashion over some period of time. We model the scenario by a dynamic Bayesian network and evaluate accuracy of next room prediction and of duration of stay, training and retraining performance, as well as memory and performance requirements of a Bayesian network predictor. The results are compared with further context predictor approaches- a state predictor and a multi-layer perceptron predictor using exactly the same evaluation set-up and benchmarks. The publicly available Augsburg Indoor Location Tracking Benchmarks are applied as predictor loads. Our results show that the Bayesian network predictor reaches a next location prediction accuracy of up to 90 % and a duration prediction accuracy of up to 87 % with variations depending on the person and specific predictor set-up. The Bayesian network predictor performs in the same accuracy range as the neural network and the state predictor.