[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128146-en":3,"doc-seo-128146-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128146,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","SOLAR IRRADIANCE FORECASTING USING STATISTICAL AND MACHINE LEARNING METHODS - Thesis","Installed solar photovoltaic (PV) capacity continues to rise worldwide and in Malaysia. In Malaysia, average daily solar radiation is about 4,000 to 5,000 Wh/m² with sunshine duration ranging from 4 to 8 hours. Output remains unstable because weather variations introduce uncertainty. Solar irradiance forecasting is therefore critical for integrating PV into the electrical grid. This research develops and compares quadratic regression, ANN, LSTM, and SARIMA models using weather–irradiance relationships and evaluates results with RMSE and correlation coefficient (R).","# SOLAR IRRADIANCE FORECASTING USING\n\nSTATISTICAL AND MACHINE LEARNINGMETHODS  \nYEW POH LENG  \n# MASTER OF SCIENCE IN ELECTRONICENGINEERING\n\nFaculty of Electronic and Computer Engineering  \n# SOLAR IRRADIANCE FORECASTING USING STATISTICALAND MACHINE LEARNING METHODS\n\nYew Poh Leng  \nMaster of Science in Electronic Engineering  \nSOLAR IRRADIANCE FORECASTING USING STATISTICAL AND MACHINELEARNING METHODS  \nYEW POH LENG  \nA thesis submitted  \nin fulfilment of the requirements forthe degree ofMaster of Science in Electronic Engineering  \nFaculty Electronic and Computer Engineering  \nUNIVERSITI TEKNIKAL MALAYSIA MELAKA  \n## DECLARATION\n\nI declare that this thesis entitled \"Solar Irradiance Forecasting using Statistical and MachineLearning Methods\" is the result of my own research except as cited in the references. Thethesis has not been accepted for any degree and isnot concurrently submitted in candidatureof anyother degree.  \nSignature :  \nName :  \nDate :  \nleng  \n\n| . . .  \u003Cbr>YEW POH LENG  \u003Cbr>. . .   |\n| --- |\n| 12-09-2023  \u003Cbr>. . .   |\n\n## APPROVAL\n\nI hereby declare that I have read this thesis and in my opinion this thesis is sufficient interms  \nof scope and quality forthe award of Master of Science in Electronic Engineering.  \nTS. DR. HO YIH HWA  \nFakulti Kejuruteraan Elektronik Dan Kejuruteraan KomputerName : Universiti. Tek nikal.Malaysia.Melaka (UTeM)Hang Tuah Jaya76100, Durian Tunggal, Melaka  \n. . . . 1.2.. S. e. p. . 2.0.2..3. . . . . . . .  \nSignature :  \nSupervisor’s  \nDate : . .  \n## DEDICATION\n\nI dedicate my thesis to my beloved family who have encouraged me through this thesis. Iam truly thankful for having my parents in my life that taught me to work hard forthe thingsthat I want todo. Besides, Iam dedicating tomy siblings who aremy sister and brother that  \nsupport me to continue my master degree.  \n## ABSTRACT\n\nThe installed capacity of solar photovoltaic (PV) is continues to rise in the world andMalaysia throughout the year. In Malaysia, the average daily solar radiation is 4,000 to 5,000  \nWh/m2, with the average daily sunshine duration ranging from 4 to 8 hours. However, the  \noutput of solar energy is lack of stability due to weather variation. Solar irradianceforecasting is a crucial component in the effective integration of solar PV systems into theelectrical grid. The variability of solar energy and the uncertainties associated with solarirradiance predictions pose significant challenges for grid operators and energy planners.This research project aims to develop advanced forecasting methods and methodologies foraccurate and reliable solar irradiance prediction, considering the specific characteristics oflocal weather conditions. The study begins by analyzing the correlation between weatherparameters and solar irradiance in the selected region, identifying the key variables thatsignificantly impact solar irradiance. Quadratic regression methods are developed toforecast solar irradiance by leveraging the relationships between weather parameters.Additionally, artificial neural network (ANN), long-short term memory (LSTM), andseasonality autoregressive integrated moving average (SARIMA) methods are evaluated todetermine their suitability for solar irradiance forecasting. Comparative analysis of thedeveloped forecasting methods is conducted using evaluation metrics such as root meansquare error (RMSE) and correlation of coefficient (R) . The performance and suitability ofdifferent statistical and machine learning techniques for solar irradiance forecastingassisting grid operators, energy planners, and policymakers in effectively integrating solarPV systems into the electrical grid and optimizing the utilization of solar energy resources.Overall, this research project aims to advance the field of solar irradiance forecasting,enabling better planning, operation, and management of solar PV systems. By reducinguncertainties in solar energy generation, it contributes to the overall advancement ofrenewable energy","cbCaifONRmCCrIY9","https://ap.wps.com/l/cbCaifONRmCCrIY9","pdf",1565988,4,1,24,"English","en",105,"# Abstract\n## Research objective\n## Data and correlation analysis\n## Forecasting methods\n## Evaluation metrics\n## Expected impact","[{\"question\":\"Why is solar irradiance forecasting important for solar PV integration?\",\"answer\":\"Solar PV output is unstable due to weather variation. Accurate forecasting supports effective integration of PV systems into the electrical grid and helps planners manage renewable energy resources.\"},{\"question\":\"Which forecasting techniques are evaluated in the research?\",\"answer\":\"The study evaluates quadratic regression, artificial neural networks (ANN), long-short term memory (LSTM), and seasonal ARIMA (SARIMA) to determine their suitability for irradiance forecasting.\"},{\"question\":\"How is the performance of the forecasting methods assessed?\",\"answer\":\"Performance is compared using evaluation metrics including root mean square error (RMSE) and the correlation coefficient (R).\"}]","SOLAR IRRADIANCE FORECASTING USING STATISTICAL AND MACHINE LEARNING METHODS - Thesis | PDF",1785945071,60,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"solar-irradiance-forecasting-using-statistical-and-machine-learning-methods-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/solar-irradiance-forecasting-using-statistical-and-machine-learning-methods-thesis/128146/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is solar irradiance forecasting important for solar PV integration?","Question",{"text":76,"@type":77},"Solar PV output is unstable due to weather variation. Accurate forecasting supports effective integration of PV systems into the electrical grid and helps planners manage renewable energy resources.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which forecasting techniques are evaluated in the research?",{"text":81,"@type":77},"The study evaluates quadratic regression, artificial neural networks (ANN), long-short term memory (LSTM), and seasonal ARIMA (SARIMA) to determine their suitability for irradiance forecasting.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the performance of the forecasting methods assessed?",{"text":85,"@type":77},"Performance is compared using evaluation metrics including root mean square error (RMSE) and the correlation coefficient (R).","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":30,"slug":109},5,"Comic","comic",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]