Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models

• Date: 01 September 2020
• Author: Zeynep Ceylan
• DOI: http://doi.org/10.1002/for.2673
• Published date: 01 September 2020

RESEARCH ARTICLE

Assessment of agricultural energy consumption of Turkey

by MLR and Bayesian optimized SVR and GPR models

Zeynep Ceylan

Industrial Engineering Department,

Samsun University, Samsun, Turkey

Correspondence

Zeynep Ceylan, Industrial Engineering

Department, Faculty of Engineering,

Samsun University, 55420, Samsun,

Turkey.

Email: zeynep.ceylan@samsun.edu.tr

Abstract

Agricultural productivity highly depends on the cost of energy required for

cultivation. Thus prior knowledge of energy consumption is an important

step for energy planning and policy development in agriculture. The aim of

the present study is to evaluate the application potential of multiple linear

regression (MLR) and machine learning tools such as support vector regres-

sion (SVR) and Gaussian process regression (GPR) to forecast the agricul-

tural energy consumption of Turkey. In the development of the models,

widespread indicators such as agricultural value-added, total arable land,

gross domestic product share of agriculture, and population data were used

as input parameters. Twenty-eight-year historical data from 1990 to 2017

were utilized for the training and testing stages of the models. A Bayesian

optimization method was applied to improve the prediction capability of

SVR and GPR models. The performance of the models was measured by

various statistical tools. The results indicated that the Bayesian optimized

GPR (BGPR) model with exponential kernel function showed a superior

prediction capability over MLR and Bayesian optimized SVR model. The

root mean square error, mean absolute deviation, mean absolute percentage

error, and coefficient of determination (R

2

) values for the BGPR model

were determined as 0.0022, 0.0005, 0.2041, and 0.9999 in the training phase

and 0.0452, 0.0310, 7.7152, and 0.9677 in the testing phase, respectively. As

a result, it can be concluded that the proposed BGPR model is an efficient

technique and has the potential to predict agricultural energy consumption

with high accuracy.

KEYWORDS

Agricultural energy, Bayesian optimization, Gaussian process regression, prediction, support

vector regression

1|INTRODUCTION

Turkey is one of the most important agricultural coun-

tries in the Europe. Agricultural products produced in

Turkey cover a wide range, from grain to medicinal plant

products. Agricultural activities provide employment

opportunities for the rural population, which is a signifi-

cant contribution to the national economy. According to

the 2018 data, Turkey has a total of approximately 20

million hectares of arable land. In the same year, 5.774

Received: 16 July 2019 Revised: 25 December 2019 Accepted: 12 January 2020

DOI: 10.1002/for.2673

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