Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

Kasım Zor,Oğuzhan Timur,Özgür Çelik,Ahmet Teke

doi:10.3390/en13051102

Kasım Zor, Oğuzhan Timur + Show 2 more

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https://doi.org/10.3390/en13051102

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Abstract

Over the past decade, energy forecasting applications not only on the grid side of electric power systems but also on the customer side for load and demand prediction purposes have become ubiquitous after the advancements in the smart grid technologies. Within this context, short-term electrical energy consumption forecasting is a requisite for energy management and planning of all buildings from households and residences in the small-scale to huge building complexes in the large-scale. Today’s popular machine learning algorithms in the literature are commonly used to forecast short-term building electrical energy consumption by generating an abstruse analytical expression between explanatory variables and response variables. In this study, gene expression programming (GEP) and group method of data handling (GMDH) networks are meticulously employed for creating genuine and easily understandable mathematical models among predictor variables and target variables and forecasting short-term electrical energy consumption, belonging to a large hospital complex situated in the Eastern Mediterranean. Consequently, acquired results yielded mean absolute percentage errors of 0.620% for GMDH networks and 0.641% for GEP models, which reveal that the forecasting process can be accomplished and formulated simultaneously via proposed algorithms without the need of applying feature selection methods.

Highlights

The ubiquity of the internet of things makes distributed energy systems smarter by optimizing energy efficiency for reducing losses and creates a new era named as the internet of energy (IoE), which is equipped with intelligent forecasting systems that employ meteorological forecasts and other explanatory information to predict future energy consumption
In the assessment of performances belonging to gene expression programming (GEP) and group method of data handling (GMDH) networks, R2, root mean squared error (RMSE), and mean absolute percentage error (MAPE) are utilized in this study
Complexity of the forecasting process comes from the fact that there are so many factors influencing building energy consumption and every building has its own characteristics, such as physical properties and operational schedule

Summary

Introduction

The ubiquity of the internet of things makes distributed energy systems smarter by optimizing energy efficiency for reducing losses and creates a new era named as the internet of energy (IoE), which is equipped with intelligent forecasting systems that employ meteorological forecasts and other explanatory information to predict future energy consumption. Energies 2020, 13, 1102 to an hour, day, or week ahead predictions, and it is considered that this concept can be applied to building electrical energy consumption forecasting as well [3]. Short-term building electrical energy consumption forecasting is an essential tool that is not merely required for the integration of smart grids to current electric power systems. It enhances a building’s quality of energy management and planning as well by monitoring energy consumption, finding base and peak demands, reducing losses, minimizing risks, securing reliability for uninterrupted operation, playing an active role in making viable decisions in regard to maintenance planning and future investments, including both renewable and non-renewable energy technologies, such as photovoltaic, landfill, and tri-generation fueled by natural gas

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