Water Demand Forecasting Based on Online Aggregation for District Meter Areas-Specific Adaption

Short-term water demand forecasting is critical to enable optimal system operation. For practical purposes, the accuracy of the forecast and the adaptability to changing conditions are paramount. Therefore, for the Battle of Water Demand Forecasting (BWDF), we propose a precise and highly flexible forecasting methodology to allow an excellent adaptation to District Meter Areas (DMA)-specific characteristics. The proposed method consists of data cleaning and pre-processing, the training of individual forecast models and finally of combining the individual forecasts by the smoothed Bernstein Online Aggregation (BOA) algorithm. The ensemble of individual forecasting models includes simple time series, high-dimensional linear, and highly non-linear models such as neural networks.