Embedding based quantile regression neural network for probabilistic load forecasting

• 作者: Chongqing KANG Dahua GAN Shuo YANG Yi WANG
• 来源期刊: 现代电力系统与清洁能源学报(英文)
• 学科: 工学
• 关键词: PROBABILISTIC load forecasting FEATURE EMBEDDING Artificial NEURAL network QUANTILE regression Machine learning
• 年，卷（期）: 2018,（2）
• 页码范围: 244-254
• 页数: 11页
• 分类号: TM715

摘要

Compared to traditional point load forecasting,probabilistic load forecasting(PLF) has great significance in advanced system scheduling and planning with higher reliability. Medium term probabilistic load forecasting with a resolution to an hour has turned out to be practical especially in medium term energy trading and can enhance the performance of forecasting compared to those only utilizing daily information. Two main uncertainties exist when PLF is implemented: the first is the temperature fluctuation at the same time of each year;the second is the load variation which means that even if observed indicators are fixed since other observed external indicators can be responsible for the variation. Therefore, we propose a hybrid model considering both temperature uncertainty and load variation to generate medium term probabilistic forecasting with hourly resolution. An innovative quantile regression neural network with parameter embedding is established to capture the load variation, and a temperature scenario based technique is utilized to generate temperatureforecasting in a probabilistic manner. It turns out that the proposed method overrides commonly used benchmark models in the case study.