PLEIAData: consumption, HVAC, temperature, weather and motion sensor data for smart buildings applications

Abstract

The current cost that energy represents is crucial in a field like climate control which has high energy demands, therefore its reduction must be prioritized. The expansion of ICT and IoT come with an extensive deployment of sensors and computation infrastructure creating an opportunity to analyze and optimize energy management. Data on building internal and external conditions is essential for developing efficient control strategies in order to minimize energy consumption while maintaining users’ comfort inside. We here present a dataset that provides key features that could be useful for a wide range of applications in the context of modeling temperature and consumption via Artificial Intelligence algorithms. The data gathering has taken place for almost 1 year in the Pleiades building of the University of Murcia, which is a pilot building of the European project PHOENIX aiming to improve building energy efficiency.

A Global Database on Whole Life Carbon, Energy and Material Intensity of Buildings(CarbEnMats-Buildings)

“…So-called whole life carbon (WLC) of buildings is gaining increasing attention among decision-makers concerned with climate and industrial policy, as well as building procurement, design, and operation. However, most open buildings datasets published thus far have been focusing on building's operational energy consumption and related parameters 1,2,[2][3][4] . Recent years furthermore brought large-scale datasets on building geometry (footprint, height) 5,6 as well as the publication of some datasets on building construction systems and material intensity 7,8 .…”

Synconn_Build: A Python Based Synthetic Dataset Generator for Testing and Validating Control-Oriented Neural Networks for Building Dynamics Prediction

“…Currently, there are several public datasets available, comprising both measured data [10][11][12][13] and synthetic data generated from simulations [14][15][16][17] . However, it is important to consider that each dataset has its own strengths and limitations.…”