The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration.

Because of the absence of lighting calculation tools at the initial stage of tunnel design, the lighting systems are usually over-dimensioned, leading to over illumination and increased energy consumption. For this reason, a fine-tuning method for switching lighting stages according to the traffic weighted L20 luminance is proposed at no additional cost. The method was applied in a real –case scenario, where L20 luminance of the access zone at eleven (11) existing tunnels was calculated. The traffic weighted method of CR14380 was used in order to calculate the actual luminance levels for the entrance zone. The new transition zone, which decreases luminance curves, was produced and compared with the existing ones. Thus, a new switching control was proposed and programed for the Supervisory Control and Data Acquisition (SCADA) system of the tunnel. The signals of the corresponding eleven L20 meters for a period of eight days were used and the corresponding annual energy consumptions were calculated using the proposed switching program for each tunnel. The results were compared with a number of scenarios in which the existing lighting system was retrofitted with Lighting Emitting Diodes (LED) luminaires. In these scenarios, the new luminaire arrangement was based not only on the existing luminance demand value for the threshold zone, but also on the newly proposed one with two different control techniques (continuous dimming and 10% step dimming). The fine-tuning method for switching resulted in energy savings between 11% and 54% depending on the tunnel when the scenario of the existing installation at no extra cost was used. Energy savings, when LED luminaires were installed, varied between 57% (for the scenario with existing luminance demand value for the threshold zone and 10% step dimming) and 85% (for the scenario with the new calculated luminance demand and continuous dimming).

In this paper, a low-order approximation (LOA) of fractional order PID (FOPID) for an automatic voltage regulator (AVR) based on the modified artificial bee colony (ABC) is proposed. The improved artificial bee colony (IABC) high-order approximation (HOA)-based fractional order PID (IABC/HOA-FOPID) controller, which is distinguished by a significant order approximation and by an integer order transfer function, requires the use of a large number of parameters. To improve the AVR system’s performance in terms of transient and frequency response analysis, the memory capacity of the IABC/HOA-FOPID controller was lowered so that it could fit better in the corrective loop. The new robust controller is named the improved artificial bee colony (IABC) low-order approximation (LOA)-based fractional order PID (IABC/LOA-FOPID). The performance of the proposed IABC/LOA-FOPID controller was compared not only to the original ABC algorithm-tuned PID controller, but also to other controllers tuned by state-of-the-art meta-heuristic algorithms such as the improved whale optimization algorithm (IWOA), particle swarm optimization (PSO), cuckoo search (CS), many optimizing liaisons (MOL), genetic algorithm (GA), local unimodal sampling (LUS), and the tree seed algorithm (TSA). Step response, root locus, frequency response, robustness test, and disturbance rejection abilities are all compared. The simulation results and comparisons with the proposed IABC/LOA-FOPID controller and other existing controllers clearly show that the proposed IABC/LOA-FOPID controller outperforms the optimal PID controllers found by other algorithms in all the aforementioned performance tests.