英文标题：
《A Comparison of Various Electricity Tariff Price Forecasting Techniques
in Turkey and Identifying the Impact of Time Series Periods》
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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：
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.
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中文摘要：
对于供应商和分销商来说，预测解除管制的电价对于在竞争激烈的市场领域制定其投标策略至关重要。在这一领域取得成功的前提是，需要准确和合适的电价预测工具。有了有效的预测工具，就可以连续有效地做出生产、销售、维护和投资决策，以实现利润和效益最大化。根据电力需求，土耳其有四种不同的电价定价；单色、日间、高峰和夜间。目标是找到预测四个电价周期的最佳合适工具，并生成短期预测（每月提前一年）。我们的方法基于找到最佳模型，确保MAPE、MAD和MSD的预测误差测量值最校我们对九名青少年的总人数中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，以查看2015年的历史值与该特定时期的预测数据是否匹配。结果表明，在给定时间序列数据的情况下，推荐的模型提供了良好的预测。实践的第二部分，我们还包括2015年，并使用2011年1月至2015年12月的时间序列计算所有模型。再次，通过选择最合适的预测模型，我们进行了预测过程，并分析了时间序列周期（2007年1月至2015年12月）的增强对我们用于预测过程的模型的影响。
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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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