This paper presents the development and implementation of a new recurrent neural network for optimization as applied to optimal operation of an electrical microgrid, which is interconnected to the utility grid; moreover, it incorporates batteries, for energy storing and supplying, and an electric car. The proposed neural network determines the optimal amount of power over a time horizon of one week for wind, solar, and battery systems, including that of the electric car, in order to minimize the power acquired from the utility grid and to maximize the power supplied by the renewable energy sources. Simulation results illustrate that generation levels for each energy source over a time horizon can be reached in an optimal form.

Module temperature is an important parameter of photovoltaic energy systems since their performance is affected by its variation. Several cooling controllers require a precise estimation of module temperature to reduce excessive heating and power losses. In this work, an adaptive neuro fuzzy inference system technique is developed for temperature estimation of photovoltaic systems. For the learning process, experimental measurements comprising six environmental variables (temperature, irradiance, wind velocity, wind direction, relative humidity, and atmospheric pressure) and one operational variable (photovoltaic power output) were used as training parameters. The proposed predictive model comprises a zero-order Sugeno neuro fuzzy system with two generalized bell-shaped membership functions per input and 128 fuzzy rules. The model is validated with experimental information from an instrumented photovoltaic system with a fitness correlation parameter of R = 95%. The obtained results indicate that the proposed methodology provides a reliable tool for estimation of modules temperature based on environmental variables. The developed algorithm can be implemented as part of a cooling control system of photovoltaic modules to reduce the efficiency losses.

In this paper, an artificial neural network inverse (ANNi) model is applied to optimize the thermal performance (η) of parabolic trough concentrators. A feedforward neural network architecture is trained using an experimental database from parabolic trough concentrators operations. Rim angle ($\phi$ _r), inlet (T_in) and outlet (T_out) fluid temperatures, ambient temperature (T_a), water flow (F_w), direct solar radiation (G_b), and the wind velocity (V_w) were used as main input variables within the neural network model to estimate the thermal performance with a correlation coefficient of R²= 0.9996 between experimental and simulated values. The sensitivity analysis is carried out to verify the effect of all input variables. The optimal operation conditions of parabolic trough concentrators are established using artificial neural network inverse modeling (ANNi) to achieve optimal operation conditions of parabolic trough concentrators. The results indicated that ANNi is a feasible tool for Parabolic Trough Concentrators optimization.