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The developments in sensing modalities and computing platforms enable many new sensing technologies and data sources for monitoring occupant presence and actions. The wealth of data opens new opportunities for extracting knowledge through data-driven modeling of occupant presence and actions. In particular, the many opportunities with machine learning techniques including supervised and unsupervised learning for classification, regression and clustering problems. Utilizing these opportunities creates new models and information relevant for generating new knowledge on multi-aspect environmental exposure, building interfaces, human behaviour, occupant-centric building design and operation. Subtask 2 of the new IEA EBC Annex 79 is addressing these opportunities and is inviting researchers and practitioners to participate. The developed data-driven models can, among others, be applied for example for calculating new schedules or models based on the actual conditions observed in buildings, data-driven analysis of the performance design versus the built, model predictive controls for buildings and fault detection and diagnostics.

The inhaled air quality in the breathing zone is strongly influenced by flow interactions around occupants. Personalized Ventilation (PV) aims to supply conditioned outdoor air directly to the occupants' breathing zone and thus improves the inhaled air quality. In this research, a Personalized Exhaust (PE) system is developed, which has two local exhaust devices installed with the chair, just above the shoulder level. Such a system placed in front of a PV system will introduce more PV air into the breathing zone of a seated person as well as exhausting part of the exhaled air from the free convective flow. This study investigates how the performance of a PV system will be affected after being integrated with the PE system. Experiments were conducted in an environmental chamber in Mixing Ventilation or Displacement Ventilation mode for the background air-conditioning system. A breathing thermal manikin was placed in the PE integrated chair in front of the PV air terminal device (ATD) to simulate a seated person in an office environment. The manikin was moved longitudinally away from the PV ATD as well as in an arc to 12 different locations. The performance of the PV-PE system at the 12 different locations was tested with regard to its ability in pulling the PV air towards a seated person moving within a small area in front of the workstation. Findings imply that the use of the combined PV-PE system for a seated person could provide more outdoor air than a PV system alone. � 2014 Elsevier Ltd.

This study presents an investigation and discussion on the energy performance of three tertiary institutional buildings in Singapore. Building information, energy consumption data of the air-conditioning system, and energy consumption data of the plug loads were collected separately. MATLAB identification models are developed to simulate the real daily energy consumption data:. Three functions are introduced to represent the function of daily occupancy, function of additional occupancy due to visitors and the function of outdoor air temperature. The results show that the predicted value follows the trend of real energy consumption value very well and can predict the daily variations. The newly developed methodology is able to simulate the daily variations of energy consumption. R-2 of 0.54, 0.66 and 0.63 can be achieved for the three buildings respectively (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

This study presents building energy forecasting methodology for cooling load for three institutional buildings. These buildings belong to a university campus in Singapore. The daily energy consumption for cooling load is obtained for a period of two years and the daily variation is analysed. The energy consumption is initially divided into five classes and the class numbers are used as inputs to develop a forecasting model. The model is developed using two machine learning tools. The tools used are Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Interface System (ANFIS). The division of data for training and testing the model is nearly 60% and 40% respectively. The results show that both ANN and ANFIS forecast the cooling load energy consumption of the three buildings with good accuracy. The correlation coefficient between measured and predicted consumption for training data are well above 0.98. The same is well above 0.96 for testing data. It is noted that such a methodology can be positively extended to other institutional buildings in the campus. (C) The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ISHVAC-COBEE 2015

Summarization: University campuses which can be viewed as small cities due to their size, users, and mixed complex activities. The energy and environmental impact caused by institutional buildings via activities and operations in teaching and research, as well as provision of support services could be considerably reduced by an effective choice of energy technologies and management. The aim of the present chapter is to outline the recent technological developments in the energy management for University Campuses. The energy technologies that are suitable for University Campus buildings, as well as the role of users, are analyzed and discussed. Two case studies are analyzed in order to present the role of energy management in the energy consumption and indoor environmental quality of campus buildings 'users. Appearing in: Comprehensive Energy Systems

Summarization: Retrofitting is widely explored as one of the energy conserving opportunities for existing buildings, in which both passive and active solutions are carefully evaluated. However, when different retrofitting solutions are combined and applied to a building, the total energy savings potential, which is less than the sum of the savings from applying the various individual retrofitting solutions, is considerably reduced and the synergies among the various technologies need to be understood and evaluated. In this study, the concept of utilizability is employed for the analysis of multiple energy retrofitting solutions in buildings and is defined as the ratio of energy savings derived from applying combined solutions to a building over the sum of individual energy savings from applying individual solutions. It is aimed at providing a better understanding of the combined retrofitting solutions. The sensitivity analysis on the utilizability value further provides a selection criterion for retrofitting solution selection. Παρουσιάστηκε στο: Journal of Civil Engineering and Management