[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119461-en":3,"doc-seo-119461-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119461,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Solar PV Power Forecasting Using Machine Learning - Master’s Thesis","Solar photovoltaic (PV) power forecasting supports efficient energy management in renewable systems. The thesis investigates multiple artificial neural network (ANN) approaches to predict PV output by incorporating key variables, and evaluates performance across different prediction horizons. Recurrent neural networks, autoencoders, and convolutional neural networks are trained and tested on real-world PV power data. Results highlight LSTM as most effective for short-term forecasting. The study further analyzes panel ageing effects using models such as linear regression, showing reliable degradation prediction. Dataset splitting into sunny and cloudy subsets and dedicated models improves accuracy through clustering.","Solar PV Power Forecasting Using Machine Learning  \nTesi di Laurea Magistrale in  \nElectrical Engineering-Ingegneria Elettrica  \nAuthor: Saloni Dhingra  \nStudent ID: 994054  \nAdvisor: Prof. Giancarlo Storti Gajani  \nAcademic Year: 2022-23  \ni  \nAbstract  \nSolar photovoltaic (PV) power forecasting is a crucial aspect of efficient energy management in the renewable energy sector. This thesis explores the application of various types of artificial neural networks (ANNs) for predicting PV power output by considering various variables that affect the power output. The proposed ANNs are used to forecast the output power for different PV technologies while considering different prediction horizons. Additionally, the impact of panel ageing is investigated using different machine learning models. To evaluate the performance of the proposed ANNs, real-world PV power data was collected and preprocessed. The preprocessed data was then used to train and test different ANNs, including recurrent neural networks, autoencoders and convolutional neural networks. The experimental results show that the proposed ANNs can accurately predict PV power output, with LSTM demonstrating the best performance for short-term forecasting. Furthermore, the impact of panel ageing on PV power was analyzed using different machine learning models, including linear regression and predictive analysis. The results show that the machine learning models can effectively predict the degradation of PV panel performance over time.  \nTo improve the accuracy of predictions, the effects of splitting the dataset into two distinct datasets-sunny and cloudy- is investigated. Furthermore, a separate prediction model is utilized for each of these datasets. The results indicate that clustering the dataset leads to improved prediction accuracy. Overall, this thesis provides a comprehensive analysis of the application of different ANNs for solar PV power forecasting and the impact of panel ageing on PV power output. The results demonstrate the potential of using machine learning techniques for accurate and reliable solar PV power forecasting.  \nKeywords: Artificial Neural Networks, Hyperparameters, Long-Short Term Memory, Panel Ageing, Dataset Clustering  \nAbstract in lingua italiana  \nLa previsione dell’energia solare fotovoltaica (PV) è un aspetto cruciale per una gestione efficiente dell’energia nel settore delle energie rinnovabili. Questa tesi esplora l’applicazionedi vari tipi di reti neurali artificiali (ANN) per prevedere l’output dell’energia solare PV, considerando diverse variabili che influenzano l’output energetico. Le ANN proposte vengono utilizzate per prevedere l’output energetico per diverse tecnologie PV, considerando diversi orizzonti di previsione. Inoltre, viene analizzato l’impatto dell’invecchiamento dei pannelli utilizzando diversi modelli di apprendimento automatico. Per valutare le prestazioni delle ANN proposte, sono stati raccolti e preelaborati dati reali sull’energia solare PV. I dati preelaborati sono stati quindi utilizzati per addestrare e testare diverse ANN, tra cui reti neurali feedforward, reti neurali ricorrenti e reti neurali convoluzionali. I risultati sperimentali mostrano che le ANN proposte possono prevedere con precisionel’output dell’energia solare PV, con il modello LSTM che offre le migliori prestazioni per laprevisione a breve termine. Inoltre, l’impatto dell’invecchiamento dei pannelli sull’output dell’energia solare PV è stato analizzato utilizzando diversi modelli di apprendimento automatico, tra cui la regressione lineare e l’analisi predittiva. I risultati mostrano che imodelli di apprendimento automatico possono prevedere in modo efficace il degrado delleprestazioni dei pannelli PV nel tempo.  \nPer migliorare l’accuratezza delle previsioni, viene studiato l’impatto della suddivisione del dataset in due sottodataset: soleggiato e nuvoloso. Inoltre, viene utilizzato un modellodi previsione dedicato per ciascun sottodataset. I risul","cbCaiaA8DZ2ixsJZ","https://ap.wps.com/l/cbCaiaA8DZ2ixsJZ","pdf",27553138,1,114,"English","en",105,"# 1 Introduction\n## 1.1 Motivation\n## 1.2 Thesis Objectives\n## 1.3 Thesis Structure\n# 2 Literature Review\n## 2.1 Solar Power Forecasting Methods\n## 2.2 Prediction Method Selection\n# 3 Data and Tools\n## 3.1 Data\n## 3.2 Implementation Tools\n# 4 Implementation and Results\n## 4.1 Data Exploration and Pre-processing","[{\"question\":\"Which machine learning models are evaluated for solar PV power forecasting?\",\"answer\":\"The thesis trains and tests several ANN types, including recurrent neural networks, autoencoders, and convolutional neural networks.\"},{\"question\":\"How does prediction horizon affect the forecasting performance?\",\"answer\":\"The proposed ANNs are evaluated using different prediction horizons, and experimental results indicate that LSTM performs best for short-term forecasting.\"},{\"question\":\"How is panel ageing handled and what do the results show?\",\"answer\":\"Panel ageing is investigated using different machine learning models, including linear regression and predictive analysis, demonstrating effective prediction of PV performance degradation over time.\"}]","Solar PV Power Forecasting Using Machine Learning - 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