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|Title:||Solar PV power forecasting considering missing data||Authors:||Zhai, Chengrui||Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2021||Publisher:||Nanyang Technological University||Source:||Zhai, C. (2021). Solar PV power forecasting considering missing data. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149619||Project:||P1029-192||Abstract:||Solar power generation has now become a mature technology and is widely used in commercial and civilian applications. In fact, our lives are inseparable from photovoltaic power (PV) generation. But photovoltaic power (PV) generation is sometimes unstable and unpredictable. In some circumstances, photovoltaic power (PV) generation will be affected by the environment, malfunction, communication and other factors, which usually leads to data instability or even loss. In case of data lost scenarios, the application performance could be dramatically degraded. This paper proposes a fresh new online training model based on the hybrid Artificial Neuron Network (ANN) machine learning to address the incomplete or missing data issue. A hybrid ensemble learning method of Extreme Learning Machine (ELM) and Random Vector Functional Link (RVFL) networks is designed as the learning algorithm for estimators and simulators. In this proposed method, a set of flawed data input will be used for offline training to restore the PV measurement and the potential imprecisely forecasted features are processed in the data validation stage to improve the overall performance further. The iteratively online training model will eliminate inaccurate predicted values and then compare the optimized value with the original value, thereby maximizing the recovery of lost data in the online mode application. Simulation results show that the new approach achieves low estimation error rate as well as short processing time.||URI:||https://hdl.handle.net/10356/149619||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
Updated on May 17, 2022
Updated on May 17, 2022
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