Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms

AbstractMachine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression algorithms are statistical and machine learning algorithms that can provide probabilistic forecasts in a straightforward way, and have not been applied so far for urban water demand forecasting. In this work, we fill this gap, thereby proposing a new family of probabilistic urban water demand forecasting algorithms. We further extensively compare seven algorithms from this family in practical one‐day ahead urban water demand forecasting settings. More precisely, we compare five individual quantile regression algorithms (i.e., the quantile regression, linear boosting, generalized random forest, gradient boosting machine and quantile regression neural network algorithms), their mean combiner and their median combiner. The comparison is conducted by exploiting a large urban water flow data set, as well as several types of hydrometeorological time series (which are considered as exogenous predictor variables in the forecasting setting). The results mostly favor the linear boosting algorithm, probably due to the presence of shifts (and perhaps trends) in the urban water flow time series. The forecasts of the mean and median combiners are also found to be skillful.