Data-driven predictive models for daily electricity consumption of academic buildings

Bilal Akbar; Khuram Pervez Amber; Anila Kousar; Muhammad Waqar Aslam; Muhammad Anser Bashir; Muhammad Sajid Khan; Bilal Akbar; Khuram Pervez Amber; Anila Kousar; Muhammad Waqar Aslam; Muhammad Anser Bashir; Muhammad Sajid Khan

doi:10.3934/energy.2020.5.783

AIMS Energy

2020, Volume 8, Issue 5: 783-801. doi: 10.3934/energy.2020.5.783

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Research article

Data-driven predictive models for daily electricity consumption of academic buildings

1.
Department of Mechanical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250, AJK, Pakistan
2.
Faculty of Engineering, Science and the Built Environment, London South Bank University, London SE1 0AA, UK
3.
Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250, AJK, Pakistan
4.
Department of Computer Systems Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250, AJK, Pakistan

Received: 27 May 2020 Accepted: 19 August 2020 Published: 27 August 2020

Academic buildings in a typical university campus occupy 42% of the total space and are responsible for nearly 50 percent of the total energy use and carbon emissions of the campus. Forecasting of energy consumption in this energy intensive building category could help higher education institutions in taking energy saving initiatives and in revising their building operating strategies. Reliable predictive techniques does not only help in forecasting a building’ energy consumption, but also help in identifying a variety of factors affecting the energy consumption of that building. This study attempts to forecast and benchmark the daily electricity consumption of an academic building situated in London, United Kingdom using two different data-driven modeling techniques, i.e., Multiple Regression and Artificial Neural Network. Hourly dataset for the electricity consumption was available for the period 2007 to 2011 from the smart meter whereas hourly data of different factors such as ambient temperature, relative humidity, wind speed and solar radiation were downloaded from the website of environmental research group of Kings College London. The performances of the two predictive models have been critically analyzed by comparing their predicted consumption with a real dataset of the same building for the year 2012. A comparison shows that both Multiple Regression (MR) and Artificial Neural Network (ANN) perform reasonably well with a Mean Absolute Percentage Error (MAPE) of 3.34% and 2.44% for working days and 5.12% and 4.59% for non-working days respectively. ANN performs slightly better than MR. This energy consumption forecasting approach can easily be adapted for predicting energy use of similar buildings.
- data-driven forecasting models,
- electricity consumption,
- academic buildings,
- ANN,
- MR
Citation: Bilal Akbar, Khuram Pervez Amber, Anila Kousar, Muhammad Waqar Aslam, Muhammad Anser Bashir, Muhammad Sajid Khan. Data-driven predictive models for daily electricity consumption of academic buildings[J]. AIMS Energy, 2020, 8(5): 783-801. doi: 10.3934/energy.2020.5.783

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Abstract

Academic buildings in a typical university campus occupy 42% of the total space and are responsible for nearly 50 percent of the total energy use and carbon emissions of the campus. Forecasting of energy consumption in this energy intensive building category could help higher education institutions in taking energy saving initiatives and in revising their building operating strategies. Reliable predictive techniques does not only help in forecasting a building’ energy consumption, but also help in identifying a variety of factors affecting the energy consumption of that building. This study attempts to forecast and benchmark the daily electricity consumption of an academic building situated in London, United Kingdom using two different data-driven modeling techniques, i.e., Multiple Regression and Artificial Neural Network. Hourly dataset for the electricity consumption was available for the period 2007 to 2011 from the smart meter whereas hourly data of different factors such as ambient temperature, relative humidity, wind speed and solar radiation were downloaded from the website of environmental research group of Kings College London. The performances of the two predictive models have been critically analyzed by comparing their predicted consumption with a real dataset of the same building for the year 2012. A comparison shows that both Multiple Regression (MR) and Artificial Neural Network (ANN) perform reasonably well with a Mean Absolute Percentage Error (MAPE) of 3.34% and 2.44% for working days and 5.12% and 4.59% for non-working days respectively. ANN performs slightly better than MR. This energy consumption forecasting approach can easily be adapted for predicting energy use of similar buildings.

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