Data-Driven Smart Buildings
DDSB
The case study examines the photovoltaic energy production of seven plants at the Politecnico di Torino (PoliTo) campus, an Italian university located in the Piedmont region. Over the years, the campus has undergone various expansions, including the recent conversion of an industrial hub into classrooms and the addition of new buildings for research centres and laboratories. To meet the increasing electricity demand of these buildings, seven photovoltaic plants have been installed across different locations on campus.
These photovoltaic plants were installed over several years, each with varying installed capacities, array configurations and monitoring infrastructures. At the time of writing (2024) the total installed photovoltaic capacity had reached nearly 1 MW. Given the size of the plants and their significant impact on the campus’s energy expenditure, a continuous energy monitoring system and an anomaly detection tool have been implemented. This system facilitates photovoltaic load forecasting and estimation and provides real-time alerts in the event of malfunctions or suboptimal performance at the site.
Scaling up an energy information system application for anomaly detection on seven photovoltaic plants; implementation and testing of continuous monitoring platform and a real time alerting tool.
High-resolution data related to seven photovoltaic (PV) plant and relative semantic metadata model. Data related to solar irradiance, voltage, current and power meters installed on the inverters with 1-minute resolution from 2014 onwards. Please, contact the authors for data availability.
Increasing awareness of energy systems operations and reducing energy waste related to incorrect and faulty operation is essential for an effective energy management strategy, especially in non-commercial and complex buildings. However, the large volume of collected data, unconventional data acquisition methods, and inefficient data storage and retrieval strategies can result in inefficient data extraction and utilisation. Advanced data analytics tasks require a formal data representation as well as a simple and logical method of transmitting feedback to end users to increase the adoption of such energy management tools. The objective of this case study is to demonstrate how advanced data analytics techniques for electrical load forecasting, a robust data acquisition pipeline, a structured metadata representation and a modern micro-services infrastructure can make data exploitation scalable and actionable for end users.
R. Chiosa, M.S. Piscitelli, A. Capozzoli. (2021). A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings. Energies, 14, 237. https://doi.org/10.3390/en14010237
M.S. Piscitelli, S. Brandi, A. Capozzoli, F. Xiao. (2021). A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings. Building Simulation, 14, 131–147. https://doi.org/10.1007/s12273-020-0650-1
A. Capozzoli, M.S. Piscitelli, S. Brandi. (2017). Mining typical load profiles in buildings to support energy management in the smart city context. Energy Procedia, 134, 865-874. https://doi.org/10.1016/j.egypro.2017.09.545
