A hybrid model for forecasting short-term electricity demand.
International Conference on AI in Finance (ICAIF)(2021)
摘要
Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. A key factor in predicting demand is weather conditions, with forecasts published every hour. We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and finally LSTM encoder-decoders to achieve higher accuracy with respect to mainstream models from the literature. HYENA decreased MAPE loss by 16\% and RMSE loss by 10\% over the best available benchmark model, thus establishing a new state of the art for the UK electric load (and price) forecasting.
更多查看译文
关键词
Hybrid models, Neural Networks, Regression, Feature Engineering
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要