Green energy is very important for developing new cities with high energy consumption, in addition to helping environment preservation. Integrating solar energy into a grid is very challenging and requires precise forecasting of energy production. Recent advances in Artificial Intelligence have been very promising. Particularly, Deep Learning technologies have achieved great results in short-term time-series forecasting. Thus, it is very suitable to use these techniques for solar energy production forecasting. In this work, a combination of a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and a Transformer was used for solar energy production forecasting. Besides, a clustering technique was applied for the correlation analysis of the input data. Relevant features in the historical data were selected using a self-organizing map. The hybrid CNN-LSTM-Transformer model was used for forecasting. The Fingrid open dataset was used for training and evaluating the proposed model. The experimental results demonstrated the efficiency of the proposed model in solar energy production forecasting. Compared to existing models and other combinations, such as LSTM-CNN, the proposed CNN-LSTM-Transformer model achieved the highest accuracy. The achieved results show that the proposed model can be used as a trusted forecasting technique that facilitates the integration of solar energy into grids.

The suspension of tiny solid particles inside the energy transport liquids could enhance their thermal conductivity as well as provide an efficient and inventive approach to significantly improve their properties of heat transport. Therefore, our aim is to explore the radiative two-dimensional unsteady flow of a viscous nanofluid about an aligned magnetic field that includes the joint effect of suction, velocity slip, and heat source across a porous convective stretching/shrinking surface. Initially, using non-dimensional variables, the nonlinear governing partial differential equations (PDEs) were transformed into ordinary differential equations (ODEs) which were subsequently solved with the help of bvp4c built-in package in MATLAB. The results declare that escalating the values of the unsteadiness parameter escalates the friction drag whereas it reduces with the escalation of the slip parameter. Furthermore, the heat transfer rate escalates with the escalation of radiation and concentration parameter, and the escalation of the heat source parameter causes to reduce the heat transfer rate. Finally, it is found that the rate of heat transfer and friction drag continuously improve and decline against the rising rates of stretching, respectively.

The heat and mass transfer in magnetized non-Newtonian Williamson nanofluid flow, saturated by gyrotactic microorganisms due to a stretched sheet, is debated here. The rough sheet is subjected to uniform heat flux, and its velocity is proportional to its distance from the slit. Nanofluid viscosity and thermal conductivity are temperature-dependent, but microbe diffusivity and Brownian motion are concentration-dependent. Through similarity transformation, the system of modeled equations is reduced to dimensionless differential equations. We employed the shooting approach in conjunction with the Runge–Kutta scheme to obtain a solution for the physical model. For various combinations of the controlling parameters, some numerical results are found. When the generated results are compared to the existing literature, the highest settlement is found. According to numerical results, the skin-friction coefficient rises as the magnetic field and thermal conductivity parameters rise, while the opposite tendency is observed for both the slip velocity and viscosity parameters.

The reasons why the model of non-Newtonian nanofluids is more applicable than other models, particularly those that take the porous medium into account, are studied here. Thus, we looked at the heat and mass transfer features of a non-Newtonian Williamson nanofluid flow due to a stretched sheet under the impact of chemical reactions, slip velocity, viscous dissipation, and the magnetic field in this article. The main focus is on a situation in which the properties of Williamson nanofluid, such as viscosity and thermal conductivity, change with temperature. After utilizing the shooting technique, a numerical solution to the suggested problem is provided. As a result, several graphs have been drawn to highlight how various physical characteristics that arise in the problems affect velocity, temperature, and concentration profiles. It was discovered that the heat and mass transmission processes are affected by the viscous dissipation phenomena, the slip velocity assumption, and the magnetic field. Theoretical and numerical results show a high level of qualitative agreement.

We ought to correct the misprinted terms in our published paper ‘Unsteady Convective MHD Flow and Heat Transfer of a Viscous Nanofluid across a Porous Stretching/Shrinking Surface: Existence of Multiple Solutions’ [Crystals2021, 11, 1359]