Artificial neural networks have demonstrated to be good at timeseries forecasting problems, being widely studied in literature. In this study an artificial neural network model is introduced for modelling the solar irradiance. Forecasting of solar irradiance is in general significant for planning the operations of power plants which convert renewable energies into electricity. In particular, the possibility to predict the solar irradiance (up to 24h or even more) can become fundamental in making power dispatching plans. In this paper, a practical method for solar irradiance forecast using artificial neural network is presented. The proposed echo state neural networks model makes it possible to forecast the solar irradiance on the base of 24h using the present values of the mean hours solar irradiance, air temperature, humidity, and the wind speed. An experimental database of solar irradiance, air temperature, humidity, wind speed data (from October 17th 2013 to May 11th 2015) has been used. The database has been collected in Lucenec (48.33N 19.67E), Slovakia.The results indicate that the proposed model performs well, while the correlation coefficient between measured and forecasted solar irradiance is in the range 0.94 − 0.97.
Modeling of quasistatic magnetic hysteresis with feed-forward neural networks Abstract. A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints. Keywords: optimal control problem with control and state constraints, adaptive critic neural network synthesis, numerial solution and example PACS: 49K15, 49M25, 49M37, 49M05, 92D40, 68X05
In this paper, a neural network-based optimal control synthesis is presented for distributed optimal control problems. We deal with solutions of systems controlled by parabolic differential equations with control and state constraints and discrete time delays. The given optimal control problem is transformed into a discrete nonlinear problem and then implemented into a feed-forward adaptive critic neural network. We propose a new algorithm to reach optimal control and an optimal trajectory using a feed-forward neural network. We present a concrete application of this simulation method on the SEIR (Susceptible—Exposed—Infectious—Recovered) optimal control problem of a distributed system for disease control. The results show that the adaptive-critic-based neural network approach is suitable for the solution of optimal distributed control problems with delay in state and control variables subject to control-state constraints and simulates the spread of disease in the SEIR system.
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