This article outlines the implementation and use of a large wireless instrumentation solution to collect data over a long time period of a few years for three collective residential buildings. The sensor network consists of a variety of 179 sensors deployed in building common areas and in apartments to monitor energy consumption, indoor environmental quality, and local meteorological conditions. The collected data are used and analyzed to assess the building performance in terms of energy consumption and indoor environmental quality following major renovation operations on the buildings. Observations from the collected data show energy consumption of the renovated buildings in agreement with expected energy savings calculated by an engineering office, many different occupancy patterns mainly related to the professional situation of the households, and seasonal variation in window opening rates. The monitoring was also able to detect some deficiencies in the energy management. Indeed, the data reveal the absence of time-of-day-dependent heating load control and higher than expected indoor temperatures because of a lack of occupant awareness on energy savings, thermal comfort, and the new technologies installed during the renovation such as thermostatic valves on the heaters. Lastly, we also provide feedback on the performed sensor network from the experiment design and choice of measured quantities to data communication, through the sensors’ technological choices, implementation, calibration, and maintenance.

—We use a sensor network of 173 sensors to monitor the energy consumption, the indoor environment quality and local meteorological conditions of three collective residential buildings composed of 62 dwellings. In particular, 146 sensors were deployed in 8 apartments and 27 sensors in the buildings common areas. The collected data are used to assess the performances of the three buildings which have recently been undergoing heavy retrofit actions. It also aims to accurately characterize some of the occupants behaviors which directly impact the buildings energy consumption and the indoor environment quality such as the occupancy patterns, the windows opening for natural ventilation or the temperature set point. We observe different occupancy patterns depending on the number of people in apartments and their schedule. As expected, we also observe a strong seasonal variation of windows opening rates and consequently in natural ventilation. Averaged indoor temperatures over the heating season are much higher than the values used in regulatory simulation scenarios. Furthermore, besides the recent buildings energy retrofit there is a total absence of time-of-day dependent heating load control which may explain a large amount of the buildings energy performance gap

We use a sensor network of 179 sensors to monitor the energy consumption, the indoor environment quality and local meteorological conditions of three collective residential buildings composed of 62 dwellings. In particular, 144 sensors were deployed in 8 apartments, 26 sensors in the buildings common areas and 9 sensors in a weather station. The collected data are used to assess the performances of the three buildings which have recently been undergoing heavy retrofit actions. It also aims to accurately characterize some of the occupants behaviors which directly impact the buildings energy consumption and the indoor environment quality such as the occupancy patterns, the windows opening for natural ventilation or the temperature set point. We observe different occupancy patterns depending on the number of people in apartments and their schedule. As expected, we also observe a strong seasonal variation of windows opening rates and consequently in natural ventilation. Averaged indoor temperatures over the heating season are much higher than the values used in regulatory simulation scenarios. Furthermore, besides the recent buildings energy retrofit there is a total absence of time-of-day dependent heating load control which may explain a large amount of the buildings energy performance gap.

Evaluation des performances de la rénovation énergétique du bâtiment à l'aide de réseaux de capteurs pour la supervision des consommations énergétiques et des usages Le secteur du bâtiment, et sa part résidentielle notamment, figurent parmi les secteurs les plus énergivores. La rénovation énergétique est un des principaux axes de réduction des consommations d’énergie. Sa réalisation repose sur un outil clé : la modélisation énergétique du bâtiment, largement employée notamment au travers de calculs règlementaires mais qui fait face à un enjeu majeur que sont les écarts de performance énergétique. Cette thèse porte sur l’étude des écarts de performance énergétique dans le cadre de la rénovation d’un cas d’étude de trois bâtiments de logements sociaux. L’objectif est l’amélioration de la calibration des modèles énergétiques par une meilleure caractérisation des consommations d’énergie et des comportements à retombées énergétiques via la collecte de données de terrain. Cette collecte est effectuée sur plus de trois ans par un réseau de capteurs comprenant 170 objets connectés, installés dans les parties communes et dans un échantillon de huit logements représentatifs. La collecte et l’analyse des données portent sur les consommations d’énergie, la qualité de l’environnement intérieure, les comportements des occupants et la météo locale. Des scénarios typiques d’exploitation des bâtiments sont extraits des données collectées et intégrés aux modèles énergétiques. Ils mettent en évidence les différences importantes entre l’exploitation réelle des bâtiments et les scénarios standardisés des études règlementaires. La calibration des modèles énergétiques permet de quantifier le poids de leurs différents paramètres sur les consommations d’énergie et contribue à améliorer leur précision au service d’une rénovation énergétique performante

We investigated clustering techniques on time series of daily electric load profiles of fourteen higher education buildings on the same campus. A k-means algorithm is implemented, and three different methods are compared: time-series features extraction with Manhattan distance and raw time series with Euclidian distance and Dynamic Time Warping. The impact of data characteristics with data collection time-steps and timeframes is studied using a database of more than 6,500 daily electric load profiles. We show that Euclidian distance applied to electric demand time series with three-month timeframes and ten-minute time-step provides the most consistent clustering results. In addition, useful insights are highlighted for non-residential buildings electric demand modeling and forecasting. Two groups of buildings can be distinguished regarding electric load profile patterns. On one hand, teaching, research, libraries, and gymnasium buildings show similar patterns distributed in two clusters corresponding to business days and closing days load profiles. On the other hand, campus office buildings present a larger number of clusters inconsistent with day-type dependent load profiles. A seasonal effect is also observed using six-month and one-year timeframes. Finally, a two-cluster distribution is obtained when aggregating all buildings load profiles. (C) 2020 Elsevier B.V. All rights reserved.

Enhancing residential buildings energy efficiency has become a critical goal to take up current challenges of human comfort, urbanization growth and the consequent energy consumption increase.In a context of integrated smart infrastructures, sensor networks offer a relevant solution to support building energy consumption monitoring, operation and prediction.The amount of accessible data with such networks also opens new prospects to better consider key parameters such as human behaviour and to lead to more efficient energy retrofit of existing buildings.However, sensor networks planning and implementation in general, and in existing buildings in particular, is a particularly complex task facing many challenges and affecting the performances of such a promising solution.In the present paper, we report on a field experiment of a sensor network deployment involving more than 250 sensors in three collective residential buildings in Paris region for the evaluation of a deep energy retrofit.More specifically, we describe the whole process of the sensor network design and roll-out and highlight the main critical aspects in such complex process.We also provide a feedback after several months of the sensor network operation and preliminary analysis of collected data.Reported results path the way for an efficient and optimized design and deployment of sensor networks for energy and indoor environment quality monitoring in existing buildings.

Enhancing residential building energy efficiency has become a critical goal to take up current challenges of human comfort, urbanization growth and the consequent energy consumption increase. In a context of integrated smart infrastructures, sensor networks offer a relevant solution to support building energy monitoring, operation and prediction. The amount of accessible data also opens new prospects to better consider key parameters such as human behavior and to lead to more efficient energy retrofit of existing buildings. However, sensor networks planning and deployment is a particularly complex task facing many challenges and affecting the performances of such a promising solution. The present paper highlights watchpoints from the implementation of an instrumentation solution for the study and evaluation of a deep energy retrofit of existing collective residential buildings. Observations will be grouped in four categories. They will be illustrated with actual field situations and discussed to provide potential solutions for efficient future sensor networks deployment.

•The review shows building energy consumption modeling and forecasting techniques.•Eight specific data-driven forecasting methods are described.•A focus is given on the data pre-processing methods and forecasting algorithms.•The supervised, unsupervised, reinforcement and machine learning tasks is discussed.

The existing stock of institutional buildings constructed before current thermal regulations is known to be high energy-consuming. In several cases, they contribute to a large share in local authorities’ expenses, especially for those dedicated to education and research. These high consumption levels are due in general to low thermal regulations requirements and to the diversity of occupants, occupancy profiles and used equipment. We hereby report on a comparative study of the energy consumption of three campus buildings covering more than 50,000m2 useful ground area and located in Paris region. Used data were collected during more than three years between 2014 and 2017 and at different time steps, from yearly down to a 10min time step. Statistical analysis tools are used, to identify the main energy drivers and their relative weight in the overall energy consumption for instance. The impact of different thermal regulations is clearly assessed through a post-occupancy study. Together with equipment, occupancy is shown to be the main electric energy consumption driver. Introduced tools lay the ground for a non-intrusive method for large tertiary buildings’ power demand curves decomposition and reconstruction.