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 regression (SVR) and Gaussian process regression (GPR) to forecast the agricultural 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 (R²) 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.

中文翻译：

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通过MLR和贝叶斯优化SVR和GPR模型评估土耳其的农业能源消耗

农业生产力在很大程度上取决于种植所需的能源成本。因此，对能源消耗的先验知识是农业能源规划和政策制定的重要步骤。本研究的目的是评估多元线性回归（MLR）和机器学习工具（如支持向量回归（SVR）和高斯过程回归（GPR））的应用潜力，以预测土耳其的农业能源消耗。在模型的开发中，广泛的指标，如农业增加值，总耕地面积，农业的国内生产总值份额和人口数据被用作输入参数。该模型的训练和测试阶段使用了1990年至2017年的28年历史数据。贝叶斯优化方法被用来提高SVR和GPR模型的预测能力。模型的性能通过各种统计工具进行了测量。结果表明，具有指数核函数的贝叶斯优化GPR（BGPR）模型具有优于MLR和贝叶斯优化SVR模型的预测能力。均方根误差，平均绝对偏差，平均绝对百分比误差和确定系数（BGPR模型的R ²）值在训练阶段确定为0.0022、0.0005、0.2041和0.9999，在测试阶段确定为0.0452、0.0310、7.7152和0.9677。结果，可以得出结论，所提出的BGPR模型是一种有效的技术，并且具有高精度预测农业能耗的潜力。