**更新：网络研讨会录制可用于：
//www.tatmou.com/videos/electricity-load-and-price-forecasting-with-matlab-81765.html.
This example demonstrates building a short term electricity load (and price) forecasting system with MATLAB®. Two non-linear regression models (Neural Networks and Bagged Regression Trees) are calibrated to forecast hourly day-ahead loads given temperature forecasts, holiday information and historical loads. The models are trained on hourly data from the NEPOOL region (courtesy ISO New England) from 2004 to 2007 and tested on out-of-sample data from 2008.

该应用程序包括（可选）Excel前端，使用户能够通过Matlab-Deployable DLL调用训练的负载预测模型。

The document titled "Introduction to Load & Price Forecasting Case Study" will guide you through the different components of the analysis.

如果您没有所有必需的工具箱，则仍然可以通过单击下面的HTML报告之一来查看运行分析的结果。

NOTE: The Access database shown in the webinar is not provided with this archive due to size restrictions. The equivalent data sets are provided in MAT-files in the folders Load\Data and Price\Data for the load and price forecasting studies respectively. The raw data files can be obtained directly from ISO New England (www.iso-ne.com.的）

更多有关负载和价格预测：
准确负载预测对于有效的运营和规划对于公用事业而言至关重要。负荷预测影响了许多决策，包括该发电机来提交给定期的，并急剧影响批发电力市场价格。负载和价格预测算法通常还具有突出的电力价格的混合式混合模型，这是模拟市场和建模能源衍生物的一些最准确的模型。电价预测也由市场参与者在许多交易和风险管理应用中广泛使用。