Mining typical load profiles in buildings to support energy management in the smart city context

Mining typical load profiles in buildings to drive energy management strategies is a fundamental task to be addressed in a smart city environment. In this work, a general framework on load profiles characterisation in buildings based on the recent scientific literature is proposed . The process relies on the combination of different pattern recognition and classification algorithms in order to provide a robust insight of the energy usage patterns at different level s and at different scales (from single building to stock of buildings). Several im plications related to energy profiling in buildings, including tariff design, demand side management and advanced energy diagnos is are discussed. Moreover, a robust methodology to mine typical energy patterns to support advanced energy diagnosis in buildin gs is introduced by analysing the monitored energy consumption of a cooling/heating mechanical room

Data mining for energy analysis of a large data set of flats

energy; statistical analysis; sustainabilit

Discovering Knowledge from a Residential Building Stock through Data Mining Analysis for Engineering Sustainability

In this paper, a dataset of 92,906 dwellings was analysed adopting data mining techniques for the classification of heating and domestic hot water primary energy demand and for the evaluation of the most influencing factors. The sample was classified in three energy demand categorical variables (Low, Medium, High) considering different geometrical and physical attributes. The output of the model made it possible to set reference threshold values among the physical variables. Moreover, high energy demand dwellings were analysed in depth using a k-means algorithm in order to evaluate the design variables which need to be considered in a refurbishment process

Enhancing energy management in buildings through data analytics technologies

L'abstract è presente nell'allegato / the abstract is in the attachmen

A holistic time series-based energy benchmarking framework for applications in large stocks of buildings

With the proliferation of Internet of Things (IoT) sensors and metering infrastructures in buildings, external energy benchmarking, driven by time series analytics, has assumed a pivotal role in supporting different stakeholders (e.g., policymakers, grid operators, and energy managers) who seek rapid and automated insights into building energy performance over time. This study presents a holistic and generalizable methodology to conduct external benchmarking analysis on electrical energy consumption time series of public and commercial buildings. Differently from conventional approaches that merely identify peer buildings based on their Primary Space Usage (PSU) category, this methodology takes into account distinctive features of building electrical energy consumption time series including thermal sensitivity, shape, magnitude, and introduces KPIs encompassing aspects related to the electrical load volatility, the rate of anomalous patterns, and the building operational schedule. Each KPI value is then associated with a performance score to rank the energy performance of a building according to its peers. The proposed methodology is tested using the open dataset Building Data Genome Project 2 (BDGP2) and in particular 622 buildings belonging to Office and Education category. The results highlight that, considering the performance scores built upon the set of proposed KPIs, this innovative approach significantly enhances the accuracy of the benchmarking process when it is compared with a conventional approach only based on the comparison with the buildings belonging to the same PSU. As a matter of fact, an average variation of about 14% for the calculated performance scores is observed for a testing set of building

Data-Driven Fault Detection and Diagnosis: Research and Applications for HVAC Systems in Buildings

The main goal of Fault Detection and Diagnosis (FDD) processes is to identify faults, determine their sources, and recognize solutions before the system is further harmed or service is lost [...

A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings

Recently, the spread of smart metering infrastructures has enabled the easier collection of building-related data. It has been proven that a proper analysis of such data can bring significant benefits for the characterization of building performance and spotting valuable saving opportunities. More and more researchers worldwide are focused on the development of more robust frameworks of analysis capable of extracting from meter-level data useful information to enhance the process of energy management in buildings, for instance, by detecting inefficiencies or anomalous energy behavior during operation. This paper proposes an innovative anomaly detection and diagnosis (ADD) methodology to automatically detect at whole-building meter level anomalous energy consumption and then perform a diagnosis on the sub-loads responsible for anomalous patterns. The process consists of multiple steps combining data analytics techniques. A set of evolutionary classification trees is developed to discover frequent and infrequent aggregated energy patterns, properly transformed through an adaptive symbolic aggregate approximation (aSAX) process. Then a post-mining analysis based on association rule mining (ARM) is performed to discover the main sub-loads which mostly affect the anomaly detected at the whole-building level. The methodology is developed and tested on monitored data of a medium voltage/low voltage (MV/LV) transformation cabin of a university campus

Enhancing energy efficiency in buildings through innovative data analytics technologies

This chapter discusses different platforms for buildings exploiting novel technologies based on sensor networks, smart meters and database management systems to collect and store energy-related data. It also discusses novel analytical tools and data mining algorithms proposed in the literature for buildings to (i) characterize energy consumption, (ii) identify the main factors that increase energy consumption, (iii) detect faults, and (iv) enhance user energy awareness. Finally, the perspectives offered by energy-related data analytics are outlined, showing how analysis techniques can be profitably exploited to enhance user energy awareness and reduce building energy consumption

Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings

Recently, a growing interest has been observed in HVAC control systems based on Artificial Intelligence, to improve comfort conditions while avoiding unnecessary energy consumption. In this work, a model-free algorithm belonging to the Deep Reinforcement Learning (DRL) class, Soft Actor-Critic, was implemented to control the supply water temperature to radiant terminal units of a heating system serving an office building. The controller was trained online, and a preliminary sensitivity analysis on hyperparameters was performed to assess their influence on the agent performance. The DRL agent with the best performance was compared to a rule-based controller assumed as a baseline during a three-month heating season. The DRL controller outperformed the baseline after two weeks of deployment, with an overall performance improvement related to control of indoor temperature conditions. Moreover, the adaptability of the DRL agent was tested for various control scenarios, simulating changes of external weather conditions, indoor temperature setpoint, building envelope features and occupancy patterns. The agent dynamically deployed, despite a slight increase in energy consumption, led to an improvement of indoor temperature control, reducing the cumulative sum of temperature violations on average for all scenarios by 75% and 48% compared to the baseline and statically deployed agent respectively