Open AccessComparative analysis of prediction algorithms for building energy usage prediction at an urban scale
Author(s) -
Usman Ali,
Mohammad Haris Shamsi,
Muhammad Nabeel,
Cathal Hoare,
Fawaz Alshehri,
Eleni Mangina,
James O’Donnell
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1343/1/012001
Subject(s) - computer science , scale (ratio) , identification (biology) , predictive modelling , energy (signal processing) , machine learning , parametric statistics , scalability , data mining , key (lock) , urban planning , predictive analytics , energy planning , building model , efficient energy use , data science , artificial intelligence , renewable energy , engineering , simulation , civil engineering , statistics , botany , physics , mathematics , computer security , electrical engineering , quantum mechanics , database , biology
Strategic planning for efficient and sustainable urban environments necessitates identification of scalable energy saving opportunities for the buildings sector. A possible resolution is the analysis of building energy use data at urban scale, although the available data is often sparse, inconsistent, diverse and heterogeneous in nature. Over the past decades, predictive modeling using sparse data has aided with the forecasting of building energy use. However, most studies of energy use prediction focus on individual buildings. This paper proposes the integration of building archetypes simulation, parametric analysis, and machine learning techniques as a solution to accurately predict individual building energy use at an urban level. The aim of the research described in this paper is to achieve accurate prediction of building energy performance, which will allow stakeholders, such as energy policymakers and urban planners, to make informed decisions when planning retrofit measures at large scale. The methodology generates synthetic building data for training the predictive model and predicts building energy use at urban scale with limited resources. The experimentation focuses on Dublin city through the development of synthetic building dataset using parametric analysis on previously identified key variables of two distinct building archetypes. Having compared different prediction algorithms, we show that the Gradient Boosted Trees algorithm gives a better prediction when compared to other algorithms.