Authors: Yogesh Singh, Amarendra Singh
DOI Link: https://doi.org/10.22214/ijraset.2022.47345
Certificate: View Certificate
The objective of this study is to give a summary of machine learning-based techniques for solar irradiation forecasting in this context. Despite the fact that numerous research describe methods like neural networks or support vector regression. Ranking the performance of such methods is difficult because of the diversity of the data collection, time step, forecasting horizon, setup, and performance indicators. The prediction inaccuracy is quite comparable overall. Others write. Global solar radiation recommended utilising ensemble forecasting or hybrid models to improve prediction accuracy. Forecasting the output power of solar systems is required for the smooth operation of the power grid or for the optimal control of the energy flows into the solar system. Prior to projecting the output of the solar system, it is essential to focus on solar irradiance. The two primary categories of methods for predicting the global solar radiation are machine learning algorithms and cloud pictures combined with physical models.
For many uses, including meteorology, hydrology, and especially the development and use of renewable solar energy systems, the global solar radiation that reaches the Earth's surface is of essential importance. However, it is not common to get direct measures of global solar radiation, particularly in developing nations. This is most likely because installing and maintaining the measuring equipment is expensive and complicated. Different methods have been developed to estimate global solar radiation because observed data are not always available. These methods include empirical models that establish linear and nonlinear relationships between meteorological variables and global solar radiation and machine learning models that simulate the complex and nonlinear mapping from meteorological variables to global solar radiationas well as satellite-based techniques for tracking the spatiotemporal variations in solar radiation on both a global and regional scale, as well as radiative transfer models to simulate the scattering and absorption of solar radiation in the atmosphere. Additionally, there are worldwide databases like Meteonorm, SolarGIS, and NASA-SSE that provide data on large-scale global sun radiation (Surface meteorology and Solar Energy). Due to their low computing costs and high prediction accuracy, respectively, empirical and machine learning models are more frequently utilised in practise among the aforementioned methodologies. The isotropic models and anisotropic models can further predict global solar radiation on PV panel surfaces with specific tilt angles based on the horizontal global solar radiation. The prediction accuracy of various types of machine learning models, particularly their computational efficiency on large-scale datasets for predicting Global solar radiation, have rarely been compared in different parts of the world. In general, machine learning models provide more accurate predictions of Global solar radiation than empirical models do. For instance, Wang et al. only evaluated the prediction accuracy of three ANN models, including the MLP, RBF, and GRNN models, for daily Global Solar Radiation estimation with Sunshine Duration and other Meteorological Variables at 12 Stations in China.. The MLP and RBF models were found to perform better than the GRNN model. At three locations in China's Hunan Province, Zou et al. tested the effectiveness of the ANFIS model for forecasting daily Global solar radiation in comparison to two empirical models (such as the Bristow-Campbell Model and Yang's Hybrid Model). The ANFIS model was shown to provide estimates of global solar radiation that were more accurate than the two empirical models. Wang et al. also contrasted the ANFIS and M5Tree models for daily estimation of the global solar radiation at 21 locations around China. According to the findings, the ANFIS model outperformed the M5Tree and empirical models. Additionally, Fan et al. contrasted two machine learning models (such as the Gradient Boost Method) for the daily forecast of global solar radiation in humid subtropical China. They discovered that the Gradient boost method models outperformed the investigated empirical models, and due to greater model stability, efficiency, and comparable prediction accuracy, they proposed the Gradient boost model as a potential machine learning model for global solar radiation estimation.
A. Machine Learning Method
A branch of computer science known as machine learning is categorised as an artificial intelligence technique. The benefit of this approach is that a model can handle problems that are impossible to represent by explicit algorithms. The reader can get a full review of some deterministic and machine learning approaches to solar forecasting. In various situations, such as pattern recognition, classification issues, spam filtering, as well as data mining and forecasting issues, machine learning models can be used because they can identify relationships between inputs and outputs even when a representation is unavailable. Because one must work with large datasets and machine learning models are capable of pre-processing and data preparation, categorization and data mining are particularly intriguing in this field. The machine learning models can then be used to forecasting issues after this stage. Machine learning is a subset of artificial intelligence, as was already explained. It focuses on the development and research of systems that can learn from data sets, enabling computers to learn without explicit programming. The methods listed below are used to forecast solar radiation.
III. RESULT AND DISCUSSION
We claim that machine learning algorithms are more effective and efficient than other traditional methods as evidenced by the results obtained. The following are some intriguing outcomes we obtained after utilising various machine learning algorithms, including Deep Learning, Decision Tree Learning, and the Generalized Learning Method.
D. Correlation & Heat Map
Correlation refers to a process for establishing the relationships between two variables. You learned a way to get a general idea about whether or not two variables are related, is to plot them on a “scatter plot”. While there are many measures of association for variables which are measured at the ordinal or higher level of measurement, correlation is the most commonly used approach.
Correlation is a statistical term describing the degree to which two variables move in coordination with one another. If the two variables move in the same direction, then those variables are said to have a positive correlation. If they move in opposite directions, then they have a negative correlation. Correlation means association - more precisely it is a measure of the extent to which two variables are related. There are three possible results of a correlational study: a positive correlation, a negative correlation, and no correlation. A heatmap (or heat map) is a graphical representation of data where values are depicted by color. They are essential in detecting what does or doesn't work on a website or product page. By experimenting with how certain buttons and elements are positioned on your website, heatmaps allow you to evaluate your product’s performance and increase user engagement and retention as you prioritize the jobs to be done that boost customer value. Heatmaps make it easy to visualize complex data and understand it at a glance.
In order to solve one of the oldest issues in the solar power sector, this study demonstrates for the first time that the prediction model (Deep Learning Method) is capable of accurately forecasting Global Solar Radiation based on hydrological, geographical, etc. characteristics. However, because different machine learning techniques have different mechanisms for making predictions, other prediction models perform less accurately with future prediction data. Nevertheless, machine learning is superior to other traditional methods in terms of ease of future data discovery and time required for prediction work. As automated data gathering becomes routine, it is possible to identify solar radiation and minimise losses by constructing, training, and testing such predictive models. As a result, we can conclude that machine learning is the ideal approach given how simple it is to use and how accurate the results are.As automated data gathering becomes routine, it is possible to identify solar radiation and minimise losses by constructing, training, and testing such predictive models. As a result, we can conclude that machine learning is the ideal approach given how simple it is to use and how accurate the results are.
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Copyright © 2022 Yogesh Singh, Amarendra Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET47345
Publish Date : 2022-11-07
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here