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Home / Special Issue / JST Vol. 32 (S1) 2024 / JST(S)-0596-2023


Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

Linnet Jaya Savarimuthu, Kirubakaran Victor, Preethi Davaraj, Ganeshan Pushpanathan, Raja Kandasamy, Ramshankar Pushpanathan, Mohanavel Vinayagam, Sachuthananthan Barathy and Vivek Sivakumar

Pertanika Journal of Science & Technology, Volume 32, Issue S1, December 2024


Keywords: Energy demand, future response, model predictive control, performance analysis, prediction, renewable energy, smart grid, system identification

Published on: 19 January 2024

The technological advancement in all countries leads to massive energy demand. The energy trading companies struggle daily to meet their customers’ power demands. For a good quality, disturbance-free, and reliable power supply, one must balance electricity generation and consumption at the grid level. There is a profound change in distribution networks due to the intervention of renewable energy generation and grid interactions. Renewable energy sources like solar and wind depend on environmental factors and are subject to unpredictable variations. Earlier, energy distribution companies faced a significant challenge in demand forecasting since it is often unpredictable. With the prediction of the ever-varying power from renewable sources, the power generation and distribution agencies are facing a challenge in supply-side predictions. Several forecasting techniques have evolved, and machine learning techniques like the model predictive controller are suitable for arduous tasks like predicting weather-dependent power generation in advance. This paper employs a Model Predictive Controller (MPC) to predict the solar array’s power. The proposed method also includes a system identification algorithm, which helps acquire, format, validate, and identify the pattern based on the raw data obtained from a PV system. Autocorrelation and cross-correlation value between input and predicted output 0.02 and 0.15. The model predictive controller helps to recognize the future response of the corresponding PV plant over a specific prediction horizon. The error variation of the predicted values from the actual values for the proposed system is 0.8. The performance analysis of the developed model is compared with the former existing techniques, and the role and aptness of the proposed system in smart grid digitization is also discussed.

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