论文标题
预测短期电力需求的混合模型
A Hybrid Model for Forecasting Short-Term Electricity Demand
论文作者
论文摘要
目前，英国电力市场受监管机构每三十分钟发布的负载（需求）预测的指导。预测需求的关键因素是天气状况，预测每小时都会发布。我们提出了HYENA：一个混合预测模型，结合了特征工程（选择候选预测指标特征），移动窗口预测变量和最后的LSTM编码器，以实现与文献中主流模型相对于文献的主流模型的更高准确性。鬣狗将MAPE损失降低了16 \％，而RMSE损失则比最佳的基准模型减少了10 \％，从而为英国电力负载（和价格）预测建立了新的最新技术。
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.
