Advanced approaches in building energy consumption prediction

Abstract

Energy consumption prediction in buildings is a major issue in the field of energy efficiency and resource management. In recent decades, the use of Artificial Intelligence (AI) has become an increasingly common approach to improve the accuracy and reliability of these predictions. In this paper, the most used digital technologies such as: AI, Big Data, Internet of Things (IoT), Blockchain, Cloud computing and 5G, which can fundamentally transform the way energy consumption predictions are made in buildings, are presented; the different types of data used in energy consumption prediction are analysed, such as weather data, energy use data and building characteristics; the various AI methods and algorithms are presented, e.g. neural networks, decision trees, support vector and machine learning algorithms, used to improve prediction accuracy. This paper focuses on a conceptual energy prediction model developed for the project "Intelligent system for predicting energy consumption in buildings (PRECONERG)", a project in the first stage of development.

Keywords

• artificial intelligence
• machine learning
• prediction model
• energy consumption