*2.3. Smart Urban Water Demand Management*

The Special Issue hosted some papers on the topic of water demand management, which is showing an increasing interest in the technical and scientific community.

A comprehensive review of urban water consumption datasets at multiple spatial and temporal scales was proposed by [9]. The recent technological developments and increasing number of pilot studies in smart water metering is resulting in an increasing availability of high-resolution metering datasets for research applications. Motivated by the need for tracking the type and accessibility of the existing water consumption datasets in the rapidly evolving field of smart metering, the authors reviewed and collected available dataset sources and classified them according to spatial and temporal scale, and dataset accessibility. In the work [9], the authors found that the existing datasets are very heterogeneous in terms of temporal and spatial scales, and they can serve different purposes depending on the scale of interest, data resolution, and related analytics, including, for instance, water demand forecast, end use disaggregation, behavioral modeling. After assembling the catalogue of existing smart meter datasets and characterizing them with the above mentioned criteria, the authors formulated a series of recommendations to support future research efforts and encourage the open access publication of smart water meter data.

A spatial aggregation effect on water demand peak factor was also in the Special Issue. The single water consumption is a random and highly volatile process. However, when aggregating a large number of consumers, temporal, but also spatial trends and patterns, can be observed. In the work [10] the peak factor for the water demand consumption as a function of spatial data aggregation on the basis of the statistical analysis of data of 1000 households was investigated. They found an empirical relation for estimating the peak factors. Furthermore, they proposed a procedure to analyze smart meter data regarding the occurring water demand peak factors and give guidance for network operators on how to process their data for design and operation.

In another contribution based on a least square support vector machine [11], the authors established a forecasting chaotic time series for short-term water demand with a forecasting horizon of one day and a time step length of 15 min. To improve the quality of the forecast, they transformed the time series of differences between the forecasted and measured data to a chaotic time series and implemented an error correction module to improve the accuracy. By testing this hybrid model on three real-world supply areas, they showed an improvement of the obtained forecasting solutions regarding mean absolute percentage error.

Another interesting paper on the smart water grid for micro-trading rainwater was proposed in the special issue by [12]. While there might be a local urban water shortage, local excess water might be available in supply areas. For non-potable water, some authors proposed to establish a smart water grid which allows to trade rainwater on a local level [12]. For doing that, they envisioned a distribution network connecting residential rainwater tanks that would enable to buy and sell rainwater on a local level (e.g., for irrigation purposes), and which would be monitored and controlled via numerous smart water sensors. In a hydraulic feasibility study, they analyzed these micro-trading and showed that water and energy savings are feasible across different climates.
