英文标题：
《A Comparison of Nineteen Various Electricity Consumption Forecasting
  Approaches and Practicing to Five Different Households in Turkey》
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作者：
T. O. Benli
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最新提交年份：
2016
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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.
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中文摘要：
家庭用电量预测的准确性对于做出更具成本效益和能效的决策至关重要。为了设计准确、合理、高效的预测模型，必须分析序列的特征。时间序列数据来源于在线Enerjisa系统，该系统是土耳其首都电能供应商的系统，用户可以通过该系统获得最近两年的用电量；在我们的研究中，时间是2014年5月至2016年5月。为了分析数据，应用了各种技术；经典分解模型；标准型，也有中心移动平均法、回归方程、指数平滑模型和ARIMA模型。在我们的研究中，九种青少年不同的方法；所有这些方法至少都有不同的方法，经过比较，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，我们在2014年5月至2016年5月期间，利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，以了解2016年6月的实际价值与该特定时期的预测价值的匹配程度。之后，我们对未来3个月的用电量进行了预测；2016年6月至9月，五户家庭各一户。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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