This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.

中文翻译：

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自适应神经模糊推理系统的电力需求时间序列预测

本文涉及使用自适应神经模糊推理系统（ANFIS）的电力需求的可靠预测。电力需求预测的需求对于电力资源规划和监控至关重要且至关重要。从加纳的电力分配公司收集了2003年至2018年期间的电力需求数据集，该数据覆盖了三个城市地区，即Mallam，Achimota和Ga East，都位于加纳。数据集分为两部分：一部分覆盖0到500小时，用于训练ANFIS算法，而另一部分用于验证。对于使用MATLAB软件进行的模拟练习，考虑了三种情况。方案1考虑了四个输入采样数据，方案2考虑了一个附加输入，使其成为5，方案3与方案1类似，不同之处在于隶属函数的数量从2增加到3。ANFIS算法的性能是通过将其预测与其他三个预测模型（即支持向量回归（SVR），最小平方支持向量机（LS-SVM）和自回归综合移动平均线（ARIMA）。研究结果表明，ANFIS算法可以准确地执行预测，随着用于训练的数据的增加，ANFIS算法收敛得更快，而隶属函数的增加会导致数据的过拟合，从而对RMSE值产生不利影响。ANFIS结果与其他先前预测电力需求的方法（包括SVR，LS-SVM，