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Annual Energy Consumption Forecasting Based on PSOCA-GRNN Model

机译：基于PSOCA-GRNN模型的年度能耗预测

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摘要

Accurate energy consumption forecasting can provide reliable guidance for energy planners and policy makers, which can also recognize the economic and industrial development trends of a country. In this paper, a hybrid PSOCA-GRNN model was proposed for the annual energy consumption forecasting. The generalized regression neural network (GRNN) model was employed to forecast the annual energy consumption due to its good ability of dealing with the nonlinear problems. Meanwhile, the spread parameter of GRNN model was automatically determined by PSOCA algorithm (the combination of particle swarm optimization algorithm and cultural algorithm). Taking China’s annual energy consumption as the empirical example, the effectiveness of this proposed PSOCA-GRNN model was proved. The calculation result shows that this proposed hybrid model outperforms the single GRNN model, GRNN model optimized by PSO (PSO-GRNN), discrete grey model (DGM (1, 1)), and ordinary least squares linear regression (OLS_LR) model.
机译：准确的能耗预测可以为能源规划人员和政策制定者提供可靠的指导，也可以识别一个国家的经济和工业发展趋势。本文提出了一种混合的PSOCA-GRNN模型进行年度能耗预测。由于其具有良好的非线性处理能力，因此采用广义回归神经网络（GRNN）模型来预测年能耗。同时，采用粒子群优化算法和文化算法相结合的PSOCA算法自动确定GRNN模型的扩散参数。以中国的年度能耗为例，证明了所提出的PSOCA-GRNN模型的有效性。计算结果表明，该混合模型优于单一GRNN模型，通过PSO优化的GRNN模型（PSO-GRNN），离散灰色模型（DGM（1，1））和普通最小二乘线性回归（OLS_LR）模型。

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