|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|5000909||1368404||2018||12 صفحه PDF||سفارش دهید||دانلود کنید|
- A new online STLF system for the Spanish Transport System Operator is presented.
- The system is focused on special days like holidays and cold and hot days.
- A detailed classification of type of days is used to enhance performance.
- Temperature information from multiple sites is included for proper modeling.
- Execution time is considered to ensure timely forecasts every hour.
This paper presents the implementation of a new online real-time hybrid load-forecasting model based on an autoregressive model and neural networks. This new system is currently running at the Spanish Transport System Operator (REE) and provides an hourly forecast for the current day and the next nine days timely every hour for the national system as well as 18 regions of Spain. These requirements impose a heavy computational burden that needs to be considered during the design phase. The system is developed to improve forecasting accuracy specifically on difficult days like hot, cold and special days. In order to achieve this goal, a deep analysis of the temperature series from 59 stations is made for each region and the relevant series are included individually in the model. Special days are also analyzed and a thorough classification of days is proposed for the Spanish national and regional system. The model is designed and tested with data from 2005 to 2015. The results provided for the period from December 2014 to October 2015 show how the addition of the proposed model to the TSO's ensemble causes a 5% RMSE overall error reduction and a 15% reduction on the 59 difficult days considered in the testing period.
Journal: Electric Power Systems Research - Volume 154, January 2018, Pages 401-412