Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportunities to reduce energy consumption and malfunctioning of the metering system. This work aims to compare the performance of several outlier detection methods, such as classical statistical methods (as boxplots) applied to the actual measurements and to the difference between the measurements and their predictions, in the task of detecting outliers in the power consumption data of a tertiary building located in France. The results show that the combination of a regression method, such as random forest, and the adjusted boxplot outlier detection method have promising potential in detecting this type of data quality problem in electricity consumption.

French regulation allows consumers in low-voltage networks to form collectives to produce, share, and consume local energy under the collective self-consumption framework. A natural consequence of collectively-owned generation projects is the need to allocate production among consumers. In long-term plans, production allocation determines each of the consumers’ benefits of joining the collective. In the short-term, energy should be dynamically allocated to reflect operation. This paper presents a framework that integrates long and short-term planning of a collective that shares a solar plus energy storage system. In the long-term planning stage, we maximize the collective’s welfare and equitably allocate expected energy to each consumer. For operation, we propose a model predictive control algorithm that minimizes short-term costs and allocates energy to each consumer on a 30-minute basis (as required by French regulation). We adjust the energy allotment ex-post operation to reflect the materialization of uncertainty. We present a case study where we showcase the framework for a 15 consumer collective.

Energy modelling is key in order to face the challenges of energy transition. There is a wide variety of modelling tools, depending on their purpose or study phase. This article summarises their main characteristics and highlights ones that are relevant when it comes to the preliminary design of energy studies at district scale. It introduces OMEGAlpes, a multi-carrier energy modelling tool to support stakeholders in the preliminary design of district-scale energy systems. OMEGAlpes is a Mixed-Integer Linear Programming (MILP) model generation tool for optimisation. It aims at making energy models accessible and understandable through its open-source development and the integration of energy stakeholders and their areas of responsibility into the models. A library of use cases developed with OMEGAlpes is presented and enables the presentation of past, current, and future development with the tool, opening the way for future developments and collaborations.

The building sector is responsible for 50% of worldwide energy consumption and 40% of CO₂ emissions. Consequently, a lot of research on Building Energy Efficiency has been carried out over recent years, covering the most varied topics. While many of these themes are no longer of interest to the scientific community, others flourish. Thus, reading trends within a field of knowledge is wise since it allows resources to be directed towards the most promising topics. However, there is a paucity of research on trend analysis in this field. Therefore, this article aims to analyse the evolution of the Building Energy Efficiency field of knowledge, identifying the recurrent themes and pointing out their trends, supported by statistical methods. Such an analysis relied on more than 9000 authors’ keywords collected from 2000 articles from the Scopus database and classified into 30 topics/themes. A frequency distribution of these themes enabled us to distinguish those most published as well as those whose academic interest has cooled down. This field of knowledge has evolved over three distinct phases, throughout which, eight themes presented an upward trend. These findings can assist researchers in optimising time and resources, investigating the topics with growing interest, and possibilities for new contributions.

Energy planning at the neighborhood level is a major development axis for the energy transition. This scale allows the pooling of production and storage equipment, as well as new possibilities for demand-side management such as flexibility. To manage this growing complexity, one needs two tools. The first concerns modeling, allowing exhaustive simulation analyses of buildings and their energy systems. The second concerns optimization, making it possible to decide on the sizing or control of energy systems. In this article, we analyze, in the case of an existing residential neighborhood, the ability to study by modeling and optimization tools two scenarios of energy flexibility of indoor heating. We propose in particular a method allowing to rely on a varied set of data available to build the various models necessary for optimization tools or dynamic simulation. A study was conducted to identify the neighborhood’s flexibility potential in minimizing $C{O}_2$ emissions, through shared physical storage, or storage in the building envelope. The results of this optimization study were then compared to their application to the virtual neighborhood by simulation.

Following the recent European directives highlighting the need to increase energy efficiency in the European Union, this work aims to show the possibility of using Non-Intrusive Load Monitoring (NILM) techniques to improve energy audits by estimating the compressed air leakage from a dataset of a tertiary building. The first step towards the reduction of energy consumption is performing an energy audit, in which a detailed analysis of the energy performance is executed. This analysis usually uses on-site measured data by the auditors. However, the time available for these measurements is limited and may not include some modes of operation. One example of that is the quantification of compressed air leaks. This task can be performed by estimating the flow rate during a no compressed air consumption period. However, these periods may not coincide with the auditors’ original schedule. This problem could be addressed by using historical data. Nevertheless, historical data from energy management systems usually are only available for global consumption, and rarely for individual appliances. In this context, a NILM approach would be helpful to enhance energy audits carrying analysis of modes of operation not included in the on-site measurements. In this paper, the leaks are firstly quantified using measurements mostly for benchmarking purposes. The results suggested 62% of leaks in the study case. In a second step, the Factorial Hidden Markov Model (FHMM) was applied to the data. Five typical working days, simulating the context of an energy audit, were used as training data, while one week during vacation time, with no compressed air consumption, was used to quantify the leaks. The results show that it was possible, in the context of an energy audit, to estimate the compressed air leakage using NILM techniques in this dataset with less than a 1% difference when compared to the estimation made with actual measurement. Finally, savings estimations considering the elimination of the leaks were performed, varying between 10% and 100% of the leakage repair. Considering the ideal scenario of complete leaks elimination, the savings would represent around 44% in the compressed air system and 4.75% of the current annual global consumption.