Panagiotis Kossieris

Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget

In water resources optimization problems, the objective function usually presumes to first run a simulation model and then evaluate its outputs. However, long simulation times may pose significant barriers to the procedure. Often, to obtain a solution within a reasonable time, the user has to substantially restrict the allowable number of function evaluations, thus terminating the search much earlier than required. A promising strategy to address these shortcomings is the use of surrogate modeling techniques. Here we introduce the Surrogate-Enhanced Evolutionary Annealing-Simplex (SEEAS) algorithm that couples the strengths of surrogate modeling with the effectiveness and efficiency of the evolutionary annealing-simplex method. SEEAS combines three different optimization approaches (evolutionary search, simulated annealing, downhill simplex). Its performance is benchmarked against other surrogate-assisted algorithms in several test functions and two water resources applications (model calibration, reservoir management). Results reveal the significant potential of using SEEAS in challenging optimization problems on a budget. Display Omitted The novel Surrogate-Enhanced Evolutionary Annealing Simplex algorithm (SEEAS) is proposed.Surrogate model is used as global search routine and for identifying promising transitions within simplex-based operators.SEEAS outperforms alternative methods in 6 test functions, in 15 & 30 dimensions and for 500 & 1000 function evaluations.SEEAS handles typical peculiarities of water optimization in hydrological calibration and multi-reservoir management.

Parameterizing residential water demand pulse models through smart meter readings

This paper proposes a method for parameterizing the Poisson models for residential water demand pulse generation, which consider the dependence of pulse duration and intensity. The method can be applied to consumption data collected in households through smart metering technologies. It is based on numerically searching for the model parameter values associated with pulse frequencies, durations and intensities, which lead to preservation of the mean demand volume and of the cumulative trend of demand volumes, at various time aggregation scales at the same time. The method is applied to various case studies, by using two time aggregation scales for demand volumes, i.e. fine aggregation scale (1?min or 15?min) and coarse aggregation scale (1?day). The fine scale coincides with the time resolution for reading acquisition through smart metering whereas the coarse scale is obtained by aggregating the consumption values recorded at the fine scale.Results show that the parameterization method presented makes the Poisson model effective at reproducing the measured demand volumes aggregated at both time scales. Consistency of the pulses improves as the fine scale resolution increases. Poisson generation models with correlated pulse intensity and duration.Parameterization aimed at preserving mean and cumulative trend of demand volumes.Multiple time aggregation scales.Smart metering readings used in applications.The higher is the smart metering accuracy, the more consistent are the generated pulses.