Journal of Green Energy and Transition to Sustainability

CFD ANALYSIS OF A FLAT PLATE SOLAR COLLECTOR TO IMPROVE HEAT TRANSFER CAPACITY

2024-08-09T11:41:33+03:00

Low-level and medium-level solar heat systems typically use flat-panel solar heat collectors to absorb solar heat energy, convert it to heat, and then heat the liquid (usually water or air) flowing through them. Communicate. These systems are used in home and industrial applications such as water and heating. The purpose of this work is to provide numerical simulations of solar collectors built for a variety of purposes. To better understand the heat transfer capacity of solar collectors, the tool Computational Fluid Dynamics
(CFD) was used in the current diploma treatise. In this paper ANSYS Workbench is used to build a 3D collector that included an air intake, a wavy textured absorption plate, a glass cover plate, and pebbles. ANSYS ICEM is used to build an unstructured grid. The results were obtained using the ANSYS FLUENT program.

PREDICTION OF SOLAR ENERGY GENETRATION FROM THE WEATHER DATA USING MACHINE LEARNING

2024-08-09T11:45:52+03:00

The significant growth of the amount of grid energy supplied by renewable sources is one of the main objectives of smart grid efforts. One of the challenges in incorporating renewable energy sources into the system is the intermittent and unpredictable nature of electricity generation. The necessity to relocate generators to meet demand as production fluctuates makes it imperative to forecast future renewable energy output. While building complex prediction models by hand for huge solar farms may be feasible, doing so for distributed power generation in the grid's millions of homes is a difficult undertaking. This research investigates machine learning methods for automatically generating site-specific forecasting models for solar power generation using National Weather Service (NWS) weather predictions in order to address the problem. by comparing several regression techniques to create prediction models, including multilayer perceptron’s and neural networks with long-term memory. combining historical NWS forecasts and sun intensity data from a weather station that has been operational for about a year to assess the accuracy of each model. Our findings demonstrate that predictive models developed for our site employing seven different weather forecasting parameters are more accurate than current forecast-based models.

WIND ENERGY FORECSTING USING RECURRENT NEURAL NETWORKS

2024-08-09T11:48:30+03:00

Forecast of wind power is an estimation of the output production required of one or more wind turbines. Because of the variations and the probabilistic characteristics of the wind energy, forecasting it accurately becomes essential for designing reliable, economic operation and power control strategies. Changes in the nature and characteristics of the wind are probabilistic and a variety of machine learning model based on statistical parameters are used to characterize the randomness in the wind power production. The
drawbacks of different approaches include their computational complexity and their inability to adapt to timeseries processes. This paper describes Recurrent Neural Network (RNN) Long-Short Term Memory (LSTM) for time series prediction of wind power. LSTM units based RNN models have the ability to learn from the important past observations and decide whether this learned information is useful for future prediction. The experimental study showed better performance of the LSTM model compared with other traditional models.

CFD ANALYSIS OF WIND TURBINE USING SHEAR STRESS TRANSFER MODEL

2024-08-09T11:52:39+03:00

A rotating machine called a wind turbine uses the wind's energy. One of the most successful forms of renewable energy has been found to be wind power. The cheap cost of wind energy is comparable to conventional energy sources like coal with current technology. Wind turbine generator systems that transform wind energy into mechanical energy depend heavily on their rotor blades. Commercial wind turbine rotor blades typically have an air foil cross section. When compared to the potential energy that can be harvested, these blades have shown to be extremely efficient at lower wind speeds. In the discipline of computational fluid dynamics (CFD), issues involving fluid flow are solved and analysed using numerical techniques and algorithms. The computations required to model the interaction of liquids and gases with surfaces specified by boundary conditions are carried out by computers in Ansys.