土耳其各种电价预测技术的比较及时间序列周期的影响识别
A Comparison of Various Electricity Tariff Price Forecasting Techniques in Turkey and Identifying the Impact of Time Series Periods
摘要：
在竞争性市场环境下，预测电价变化对供应商和分销商制定竞价策略至关重要。在这一领域取得成功的前提是需要准确、适用的电价预测工具。在有效的预测工具的存在下，以利润和效益最大化为目标的生产、销售、维修和投资决策可以成功而有效地完成。根据电力需求，土耳其有四种不同的电价定价;单色，白天，高峰和夜晚。目标是找到最合适的工具来预测电力的四个定价期，并产生短期预测（一年前每月）。我们的方法基于找到最佳模型，确保最小的预测误差测量：MAPE，MAD和MSD。我们对九个青少年的各种预测方法进行了比较，至少所有这些方法都有不同的方法论方面。我们的第一步是对2015年进行预测。我们验证并分析了最佳模型的性能，并进行了比较，以了解2015年的历史值与该特定时期的预测数据的匹配程度。结果表明，给定的时间序列数据，推荐的模型提供了良好的预测。第二部分的实践，我们还包括2015年，并计算所有的模型与时间序列的2011年1月至2015年12月。再次通过选择最合适的预测模型，我们进行了预测过程，并分析了增强时间序列周期（2007年1月至2015年12月）对我们用于预测过程的模型的影响。
英文摘要：
It is very vital for suppliers and distributors to predict the deregulated electricity prices for creating their bidding strategies in the competitive market area. Pre requirement of succeeding in this field, accurate and suitable electricity tariff price forecasting tools are needed. In the presence of effective forecasting tools, taking the decisions of production, merchandising, maintenance and investment with the aim of maximizing the profits and benefits can be successively and effectively done. According to the electricity demand, there are four various electricity tariffs pricing in Turkey; monochromic, day, peak and night. The objective is find the best suitable tool for predicting the four pricing periods of electricity and produce short term forecasts (one year ahead-monthly). Our approach based on finding the best model, which ensures the smallest forecasting error measurements of: MAPE, MAD and MSD. We conduct a comparison of various forecasting approaches in total accounts for nine teen, at least all of those have different aspects of methodology. Our beginning step was doing forecasts for the year 2015. We validated and analyzed the performance of our best model and made comparisons to see how well the historical values of 2015 and forecasted data for that specific period matched. Results show that given the time-series data, the recommended models provided good forecasts. Second part of practice, we also include the year 2015, and compute all the models with the time series of January 2011 to December 2015. Again by choosing the best appropriate forecasting model, we conducted the forecast process and also analyze the impact of enhancing of time series periods (January 2007 to December 2015) to model that we used for forecasting process.
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。
