Sapnken Flavian Emmanuel, Kibong Marius Tony, Tamba Jean Gaston
Laboratory of Technologies and Applied Science, IUT Douala, P.O. Box 8698, Douala, Cameroon.
Transports and Applied Logistics Laboratory, University Institute of Technology, University of Douala, P.O. Box 8698, Douala, Cameroon.
MethodsX. 2023 Jun 15;11:102259. doi: 10.1016/j.mex.2023.102259. eCollection 2023 Dec.
It is crucial to develop highly accurate forecasting techniques for electricity consumption in order to monitor and anticipate its evolution. In this work, a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)) is proposed. A linear corrective term is included in the conventional GMC(1,N) structure, parameter estimation is carried out in a manner consistent with the modelling process, and an iterative technique is used to get the cumulated forecasting function of ODGMC(1,N). As a result, the forecasting capacity of ODGMC(1,N) is more reliable and its stability is enhanced. For validation purposes, ODGM(1,N) is applied to forecast Cameroon's annual electricity demand. The results show that the novel model scores 1.74% MAPE and 132.16 RMSE and is more precise than competing models.•ODGMC(1,N) corrects the linear impact of on the forecasting performance.•Wavelet transform is used to remove irrelevant information from input data.•The proposed model can be used to track annual electricity demand.
为了监测和预测电力消耗的演变，开发高度准确的预测技术至关重要。在这项工作中，提出了离散灰色多元卷积模型（ODGMC(1,N)）的一种新形式。在传统的GMC(1,N)结构中包含一个线性校正项，以与建模过程一致的方式进行参数估计，并使用迭代技术获得ODGMC(1,N)的累积预测函数。结果，ODGMC(1,N)的预测能力更可靠，其稳定性得到增强。为了进行验证，将ODGM(1,N)应用于预测喀麦隆的年度电力需求。结果表明，新模型的平均绝对百分比误差（MAPE）为1.74%，均方根误差（RMSE）为132.16，比竞争模型更精确。•ODGMC(1,N)校正了[未提及具体内容]对预测性能的线性影响。•使用小波变换从输入数据中去除无关信息。•所提出的模型可用于跟踪年度电力需求。
