Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions.

The Paris Agreement, a landmark international treaty signed in 2016 to limit global warming to 2°C, has urged researchers to explore various strategies for achieving its ambitious goals. While Renewable Energy (RE) innovation holds promise, it alone may not be sufficient as critical deadlines approach. This field of research presents numerous challenges, foremost among them being the costliness of materials involved. However, emerging advancements in Machine Learning (ML) technologies provide a glimmer of hope; these sophisticated algorithms can accurately predict the output of energy systems without relying on physical resources and instead leverage available data from diverse energy platforms that have emerged over recent decades. The primary objective of this paper is to comprehensively explore various ML techniques and algorithms in the context of Renewable Energy Systems (RES). The investigation will address several vital inquiries, including identifying and evaluating existing RE technologies, assessing their potential for further advancement, and thoroughly analyzing the challenges and limitations associated with their deployment and testing. Furthermore, this research examines how ML can effectively overcome these obstacles by enhancing RES performance. By identifying future research opportunities and outlining potential directions for improvement, this work seeks to contribute to developing environmentally sustainable energy systems.

New Concept of Power Generation Using TEGs: Thermal Modeling, Parametric Analysis, and Case Study.

In this manuscript, an innovative concept of producing power from a thermoelectric generator (TEG) is evaluated. This concept takes advantage of using the exhaust airflow of all-air heating, ventilating, and air-conditioning (HVAC) systems, and sun irradiation. For the first step, a parametric analysis of power generation from TEGs for different practical configurations is performed. Based on the results of the parametric analysis, recommendations associated with practical applications are presented. Therefore, a one-dimensional steady-state solution for the heat diffusion equation is considered with various boundary conditions (representing applied configurations). It is revealed that the most promising configuration corresponds to the TEG module exposed to a hot fluid at one face and a cold fluid at the other face. Then, based on the parametric analysis, the innovative concept is recognized and analyzed using appropriate thermal modeling. It is shown that for solar radiation of 2000 W/m2 and a space cooling load of 20 kW, a 40 × 40 cm2 flat plate is capable of generating 3.8 W of electrical power. Finally, an economic study shows that this system saves about $6 monthly with a 3-year payback period at 2000 W/m2 solar radiation. Environmentally, the system is also capable of reducing about 1 ton of CO2 emissions yearly.