Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

Seyed Azad Nabavi,Martha A Zaidan,Naser Hossein Motlagh,Sahar Mohammadi,Majid Zandi,Alireza Aslani

doi:10.3390/en13195171

Seyed Azad Nabavi, Martha A Zaidan + Show 4 more

Open Access

https://doi.org/10.3390/en13195171

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Abstract

Energy has a strategic role in the economic and social development of countries. In the last few decades, energy demand has been increasing exponentially across the world, and predicting energy demand has become one of the main concerns in many countries. The residential and commercial sectors constitute about 34.7% of global energy consumption. Anticipating energy demand in these sectors will help governments to supply energy sources and to develop their sustainable energy plans such as using renewable and non-renewable energy potentials for the development of a secure and environmentally friendly energy system. Modeling energy consumption in the residential and commercial sectors enables identification of the influential economic, social, and technological factors, resulting in a secure level of energy supply. In this paper, we forecast residential and commercial energy demands in Iran using three different machine learning methods, including multiple linear regression, logarithmic multiple linear regression methods, and nonlinear autoregressive with exogenous input artificial neural networks. These models are developed based on several factors, including the share of renewable energy sources in final energy consumption, gross domestic production, population, natural gas price, and the electricity price. According to the results of the three machine learning methods applied in our study, by 2040, Iranian residential and commercial energy consumption will be 76.97, 96.42 and 128.09 Mtoe, respectively. Results show that Iran must develop and implement new policies to increase the share of renewable energy supply in final energy consumption.

Highlights

Energy is a vital need for contemporary human life and has profound impressions on social security, welfare as well as sturdy economic growth and development of a nation [1,2]
Iranian Residential and Commercial Energy Consumption (RCEC) is predicted by Multi-Linear Regression (MLR), Logarithmic Linear Regression (LMLR), and Nonlinear AutoregRessive with eXogenous input (NARX) models
The MLR method forecasts a drastic increase in RCEC while LMLR and NARX methods anticipate

Summary

Introduction

Energy is a vital need for contemporary human life and has profound impressions on social security, welfare as well as sturdy economic growth and development of a nation [1,2]. Despite the high cost and scarcity of energy [3], the growing world population has raised the global energy demand, especially in residential and commercial sectors. Residential and commercial buildings are the main sources of energy consumption in different countries [5]. Energy modeling which involves the effective use of energy resources is used to achieve sustainable development. To decrease energy demand, technical, organizational, and behavioral statistics are important factors to be considered. A well-known example of modeling energy consumption is using a top-down and a bottom-up approach. These approaches rely on different levels of information, calculation and simulation. We review the research works which have used these approaches in their studies

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