Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

被引:0
作者
Savarimuthu, Linnet Jaya [1 ]
Victor, Kirubakaran [1 ]
Davaraj, Preethi [2 ]
Pushpanathan, Ganeshan [3 ]
Kandasamy, Raja [4 ]
Pushpanathan, Ramshankar [5 ]
Vinayagam, Mohanavel [6 ]
Barathy, Sachuthananthan [7 ]
Sivakumar, Vivek [8 ]
机构
[1] Gandhigram Rural Inst Deemed Be Univ, Ctr Rural Energy, Gandhigram 624302, Tamil Nadu, India
[2] PSNA Coll Engn & Technol, Dept Comp Sci & Engn, Dindigul 624622, Tamil Nadu, India
[3] Sri Eshwar Coll Engn, Dept Mech Engn, Coimbatore 641202, Tamil Nadu, India
[4] Anna Univ Reg Campus Coimbatore, Dept Mech Engn, Coimbatore 641046, Tamil Nadu, India
[5] Anna Univ, Coll Engn Guindy, Dept Civil Engn, Chennai 600025, Tamil Nadu, India
[6] Bharath Inst Higher Educ & Res, Ctr Mat Engn & Regenerat Med, Chennai 600073, Tamil Nadu, India
[7] Sree Vidyanikethan Engn Coll, Dept Mech Engn, Tirupati 517102, Andhra Pradesh, India
[8] GMR Inst Technol, Dept Civil Engn, Razam 532127, Andra Pradesh, India
来源
PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY | 2024年 / 32卷
关键词
Energy demand; future response; model predictive control; performance analysis; prediction; renewable energy; smart grid; system identification; OPTIMAL OPERATION; POWER OUTPUT; MODEL; WEATHER; SYSTEM; RADIATION; BUILDINGS; RESOURCES; FORECAST;
D O I
10.47836/pjst.32.S1.05
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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
引用
收藏
页码:69 / 92
页数:24
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