土耳其5个不同家庭19种用电量预测方法和实践的比较
A Comparison of Nineteen Various Electricity Consumption Forecasting Approaches and Practicing to Five Different Households in Turkey
摘要：
家庭用电量预测的准确性对于采取更好的成本效益和能源效率决策至关重要。为了设计准确、合理、有效的预测模型，必须对序列的特征进行分析。时间序列数据的来源来自在线Enerjisa系统，该系统是土耳其首都的电力能源供应商，消费者可以达到他们最近两年的电力消费量;在我们的研究中，该期间为2014年5月至2016年5月。各种技术已被应用，以分析数据;经典的分解模型;标准型，也与中心移动平均法，回归方程，指数平滑模型和ARIMA模型。在我们的研究中，九种不同的方法，所有这些都至少有不同的方法学方面，进行了比较，并通过考虑MAPE，MAD和MSD的最小值来确定最佳预测模型。作为第一步，我们选择了2014年5月至2016年5月的时间段，并使用最佳预测模型找到了2016年6月的预测值。在找到2016年6月的最佳预测模型和拟合值后，进行了验证过程;我们进行了比较，以查看2016年6月的实际值与该特定时期的预测值是否匹配。之后，我们对接下来的3个月进行了用电量预测; 2016年6月至9月，分别为五个家庭。
英文摘要：
The accuracy of the household electricity consumption forecast is vital in taking better cost effective and energy efficient decisions. In order to design accurate, proper and efficient forecasting model, characteristics of the series have to been analyzed. The source of time series data comes from Online Enerjisa System, the system of electrical energy provider in capital of Turkey, which consumers can reach their latest two year period electricity consumptions; in our study the period was May 2014 to May 2016. Various techniques had been applied in order to analyze the data; classical decomposition models; standard typed and also with the centering moving average method, regression equations, exponential smoothing models and ARIMA models. In our study, nine teen different approaches; all of these have at least diversified aspects of methodology, had been compared and the best model for forecasting were decided by considering the smallest values of MAPE, MAD and MSD. As a first step we took the time period May 2014 to May 2016 and found predicted value for June 2016 with the best forecasting model. After finding the best forecasting model and fitted value for June 2016, than validating process had been taken place; we made comparisons to see how well the real value of June 2016 and forecasted value for that specific period matched. Afterwards we made electrical consumption forecast for the following 3 months; June-September 2016 for each of five households individually.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
