Research Article Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix P. Dhanalakshmi, 1 V. Venkatesh, 2 P. S. Ranjit, 3 N. Hemalatha, 4 S. Divyapriya, 5 R. Sandhiya, 6 Sumit Kushwaha, 7 Asmita Marathe, 8 and Mekete Asmare Huluka 9 1 Department of Computer Science and Systems Engineering, Sree Vidyanikethan Engineering College (SVEC), Tirupati, Andhra Pradesh 517102, India 2 Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu 602105, India 3 Department of Mechanical Engineering, Aditya Engineering College, Surampalem, Andhra Pradesh 533437, India 4 Institute of Electronics and Communication Engineering, Saveetha School of Engineering (SIMATS), Chennai, Tamil Nadu 600124, India 5 Department of Electrical and Electronics Engineering, Karpagam Academy of Higher Education, Eachanari, Tamil Nadu 641021, India 6 Department of Computer Science Engineering, RMK College of Engineering and Technology (RMKCET), Thiruvallur, Tamil Nadu 601206, India 7 Department of Computer Applications, University Institute of Computing, Chandigarh University, Punjab 140413, India 8 Department of Technology, Savitribai Phule Pune University, Pune, Maharashtra 411007, India 9 Department of Electrical and Computer Engineering, Institute of Technology, University of Gondar, Gondar, Ethiopia Correspondence should be addressed to Mekete Asmare Huluka; mekete.asmare@uog.edu.et Received 19 July 2022; Revised 13 September 2022; Accepted 19 September 2022; Published 14 October 2022 Academic Editor: BR Ramesh Bapu Copyright © 2022 P. Dhanalakshmi et al. 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. In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the- art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial. 1. Introduction Researchers in the field of meteorology have been interested in solar radiation for centuries. Irradiance forecasting has produced precise and accurate results in a number of recently conducted studies as a result of a variety of recently developed technologies [1]. PV is a technology that has been steadily increasing its share in the global power generation industry, which has made it a key player in the global energy market. This industry has experienced consistent growth over the past ten to twelve years, with more than one hundred gigawatts of new grid-connected capacity being added in just the year 2018 [2]. As a consequence of this, a significant number of PV deployments that are currently taking place and those that are anticipated to take place in the near future imply significant levels of PV penetration Hindawi International Journal of Photoenergy Volume 2022, Article ID 5756610, 9 pages https://doi.org/10.1155/2022/5756610