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Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm

Goudarzi, Shidrokh and Anisi, Mohammad Hossein and Kama, Nazri and Doctor, Faiyaz and Soleymani, Seyed Ahmad and Sangaiah, Arun Kumar (2019) 'Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm.' Energy and Buildings, 196. 83 - 93. ISSN 0378-7788

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Abstract

Overall energy consumption has expanded over the previous decades because of rapid population, urbanization and industrial growth rates. The high demand for energy leads to higher cost per unit of energy, which, can impact on the running costs of commercial and residential dwellings. Hence, there is a need for more effective predictive techniques that can be used to measure and optimize energy usage of large arrays of connected Internet of Things (IoT) devices and control points that constitute modern built environments. In this paper, we propose a lightweight IoT framework for predicting energy usage at a localized level for optimal configuration of building-wide energy dissemination policies. Autoregressive Integrated Moving Average (ARIMA) as a statistical liner model could be used for this purpose; however, it is unable to model the dynamic nonlinear relationships in nonstationary fluctuating power consumption data. Therefore, we have developed an improved hybrid model based on the ARIMA, Support Vector Regression (SVRs) and Particle Swarm Optimization (PSO) to predict precision energy usage from supplied data. The proposed model is evaluated using power consumption data acquired from environmental actuator devices controlling a large functional space in a building. Results show that the proposed hybrid model out-performs other alternative techniques in forecasting power consumption. The approach is appropriate in building energy policy implementations due to its precise estimations of energy consumption and lightweight monitoring infrastructure which can lead to reducing the cost on energy consumption. Moreover, it provides an accurate tool to optimize the energy consumption strategies in wider built environments such as smart cities.

Item Type: Article
Uncontrolled Keywords: Energy consumption, Algorithm design, Prediction
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TD Environmental technology. Sanitary engineering
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 17 May 2019 09:55
Last Modified: 17 May 2019 09:55
URI: http://repository.essex.ac.uk/id/eprint/24593

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