Scientific Publications

Date: 2023

Publication type: Research article.

Author(s): Konstantinos Touloumis, Panagiotis Kapsalis, Elissaios Sarmas, Stathis Stamatopoulos, Vangelis Marinakis and Haris Doukas

Keywords: Building Energy, Analytics, Big Data Pipeline, Energy Data Modelling, Building Data Processing

Abstract: Collecting data for providing energy analytics services has closely been dependent on Building Management Systems (BMSs). A BMS connected to a set of sensors and other electronic devices aims at observing a collection of rooms in terms of temperature, humidity, and other energy related metrics with the goal of maximining user comfort while preserving energy consumption. The tasks of collecting, preprocessing, storing and querying data on a BMS faces the same challenges with those of querying big data in terms of scalability, data heterogeneity, and integration. This paper presents a framework that addresses the problem of collecting sensor data in one enriched data warehouse which follows a common ontology model. Data from various data sources is homogenized by appropriate data preprocessing and feature extraction techniques. The framework allows timeseries-oriented querying with the target outcome of providing stakeholders high detailed analytics services for decision making on energy consumption optimization, building renovation and financing.

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