The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, a hybrid monthly-annual inflow forecasting approach is proposed and tested within a model predictive control framework for the long-term hydropower scheduling (LTHS). The inflow forecasts are provided on a monthly basis for a short horizon (close to present) and on an annual basis for the remaining optimization horizon, up to three years. The tests are conducted in a simulation environment...
This study presents a prediction system based on evolving fuzzy models and a bottom-up approach for daily streamflow forecasting. Prediction models are based on adaptive Takagi-Sugeno fuzzy inference systems. These models make use of a sequential learning approach for updating their own structure and parameters over time according to changes or variations identified in the series, whereas rainfall...
This paper proposes an operational policy for long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models using an open-loop feedback control framework. The optimization model precisely represents hydropower generation by taking into consideration water head as a nonlinear function of storage, discharge and spillage. The inflow is made available...
This paper presents a predictive control approach for long-term generation scheduling of hydro-thermal power systems. The approach is based on an open-loop feedback control scheme that uses a neural fuzzy network forecasting model, for handling the stochastic nature of inflows, and a deterministic nonlinear optimization model, to determine the discharge decisions to be implemented. As a consequence,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.