Scenario-based model predictive control of water reservoir systems

The optimal operation of water reservoir systems is a challenging task involving multiple conflicting objectives. The main source of complexity is the presence of the water inflow, which acts as an exogenous, highly uncertain disturbance on the system. When model predictive control (MPC) is employed, the optimal water release is usually computed based on the (predicted) trajectory of the inflow. This choice may jeopardize the closed-loop performance when the actual inflow differs from its forecast. In this work, we consider - for the first time - a stochastic MPC approach for water reservoirs, in which the control is optimized based on a set of plausible future inflows directly generated from past data. Such a scenario-based MPC strategy allows the controller to be more cautious, counteracting droughty periods (e.g., the lake level going below the dry limit) while at the same time guaranteeing that the agricultural water demand is satisfied. The method's effectiveness is validated through extensive Monte Carlo tests using actual inflow data from Lake Como, Italy.Comment: Modeling, Estimation and Control Conference, Lake Tahoe, Nevada, USA, October 2-5 202

Split-Boost Neural Networks

The calibration and training of a neural network is a complex and time-consuming procedure that requires significant computational resources to achieve satisfactory results. Key obstacles are a large number of hyperparameters to select and the onset of overfitting in the face of a small amount of data. In this framework, we propose an innovative training strategy for feed-forward architectures - called split-boost - that improves performance and automatically includes a regularizing behaviour without modeling it explicitly. Such a novel approach ultimately allows us to avoid explicitly modeling the regularization term, decreasing the total number of hyperparameters and speeding up the tuning phase. The proposed strategy is tested on a real-world (anonymized) dataset within a benchmark medical insurance design problem

Integrated real-time control of water reservoirs with deterministic and probabilistic multi-timescale forecasts: Application to the Lake Como

Hydro-meteorological forecasts are more and more easily available with improving skill over longer timescales and with higher spatiotemporal resolutions. Their uncertainties are commonly represented by ensemble prediction systems which are now dominant at the global and continental scale, while short-term deterministic forecasts are still used, especially in some local contexts. For effective water resources management, it is critical to understand how to use the wealth of information provided by different forecast systems over multiple timescales to help meet the water demand of key competing socio-economic sectors, while reducing short-term impacts and bringing the controlled systems to desirable states in the long term. Real-time control schemes of water reservoirs like Model Predictive Control (MPC) can help meet these goals, by providing a flexible framework to use forecasts proactively and satisfy multiple competing objectives while respecting operational constraints. In this study, we propose a new nested multi-stage stochastic MPC framework integrating the use of both deterministic and ensemble hydrological forecasts over multiple timescales from short-term (60 hours) to seasonal (7 months ahead). We demonstrate the performance of this real-time controller for the Lake Como system, located in the Italian Alps, where a large lake is regulated mainly for irrigation supply and flood control. First, seasonal ensemble forecasts are used to solve a Tree-Based MPC (TB-MPC) problem optimising the reservoir management over several months, by adopting a tree structure to summarise the ensemble information including the resolution of uncertainty in time. Second, the decisions identified so far are used to condition daily operations over a month using sub-seasonal probabilistic forecasts (up to 46 days) under the same TB-MPC approach. Third, the decisions for the first few days are then further adapted to optimize operations three days ahead using deterministic short-term forecasts with MPC. The sub-seasonal and seasonal ensemble (re-)forecasts used are those produced by the European Flood Awareness System (EFAS) from the Copernicus Emergency Management Service which uses ensemble meteorological forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). While EFAS is uncalibrated for the study area, we apply bias-correction techniques to improve the agreement of forecasts with local observations and allow their use for resolving the water-balance within MPC. The short-term 60-h forecasts come from a locally calibrated hydrological model (TOPKAPI) using deterministic weather forecasts from the COSMO (Consortium for Small Scale Modelling) model. The skill of all these forecasts is assessed, as well as the ensemble spread–error relationship for EFAS at different lead times. To evaluate the value of the forecasts we compare the performance of the real-time MPC controller with different benchmarks including perfect forecasts, climatology, and persistence. Finally, we investigate the link between forecast skill and value for reservoir operation, and we compare the performance of the nested MPC framework integrating multi-timescale forecasts with the MPC using single forecasts