[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290879-105":59,"doc-detail-290879-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","advanced-theory-and-applications-in-time-series-analysis-smart-grid-electricity-demand-forecasting-using-weather-based-midas-and-machine-learning-models-the-case-of-new-zealand","Advanced Theory and Applications in Time Series Analysis - Smart grid electricity demand forecasting using weather-based MIDAS and machine learning models: the case of New Zealand","","This study builds a comparative forecasting framework that combines daily weather information with quarterly electricity generation, treated as a proxy for electricity demand in New Zealand, using mixed-frequency modelling. The workflow moves from baseline univariate time-series models and classical mixed data sampling regressions to advanced regularised autoregressive mixed-frequency models and machine learning mixed-frequency methods. Results show mixed-frequency approaches outperform univariate benchmarks when higher-frequency weather signals are used. Autoregressive mixed-frequency models perform best on shorter recent windows, while SARIMA stays highly competitive and achieves the lowest RMSE over the main eight-quarter horizon. Machine learning mixed-frequency models show mixed outcomes due to dimensionality and limited sample size. The framework yields interpretable forecasts for planning in renewable-heavy systems.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/advanced-theory-and-applications-in-time-series-analysis-smart-grid-electricity-demand-forecasting-using-weather-based-midas-and-machine-learning-models-the-case-of-new-zealand/290879/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/advanced-theory-and-applications-in-time-series-analysis-smart-grid-electricity-demand-forecasting-using-weather-based-midas-and-machine-learning-models-the-case-of-new-zealand/290879.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the study integrate weather data with electricity generation information?","Question",{"text":112,"@type":113},"It uses mixed-frequency modelling to combine daily weather variables with quarterly electricity generation, treated as a demand proxy for New Zealand.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which modelling family performs best, and under what evaluation window?",{"text":117,"@type":113},"Autoregressive mixed-frequency models deliver strong forecasting performance, especially over shorter recent evaluation windows.",{"name":119,"@type":110,"acceptedAnswer":120},"Why do machine learning-based mixed-frequency models show mixed results?",{"text":121,"@type":113},"Their performance is mixed, likely due to challenges from data dimensionality and limited sample size.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290879,1790508467,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":36,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Japanese Journal of Statistics and Data Science [https://doi.org/10.1007/s42081-026-00353-1](https://doi.org/10.1007/s42081-026-00353-1)  \nORIGINAL PAPER  \nAdvanced Theory and Applications in Time Series Analysis  \nSmart grid electricity demand forecasting using weather-based MIDAS and machine learning models: the case of New Zealand  \nRogith Ramesh Babu1 · Nuttanan Wichitaksorn1 · Shu Su1 · Clarissa Cortes Pires2 · Edna Lu2  \nReceived: 20 February 2026 / Revised: 12 May 2026 / Accepted: 20 May 2026 © The Author(s) 2026  \nAbstract  \nThis study develops a comparative forecasting framework that integrates daily weather information with quarterly electricity generation, used here as a proxy for electricity demand in New Zealand, through mixed-frequency modelling approaches. The analysis progresses from baseline univariate time-series models to classical mixed data sampling regressions, advanced regularisedand autoregressive mixed-frequency models, and machine learning-based mixed-frequency methods. The forecasting results show that mixed-frequency models can improve upon traditional univariate benchmarks by incorporating higher-frequency weather information. Among the advanced approaches, autoregressive mixed-frequency models deliver strong forecasting performance, particularly over shorter recent evaluation windows, while seasonal time-series benchmarks such as SARIMA remain highly competitive and achieve the lowest RMSE in the main eight-quarter evaluation period. Machine learning-based mixedfrequency models show mixed performance, likely reflecting the challenges posed by data dimensionality and limited sample size. The proposed framework provides interpretable forecasts and offers practical insights for electricity system planning in renewable-dominated energy systems.  \nKeywords Electricity demand forecasting · Mixed-frequency modelling · Weather-based forecasting · Machine learning · New Zealand energy system  \nB Nuttanan Wichitaksorn [nuttanan.wichitaksorn@aut.ac.nz](nuttanan.wichitaksorn@aut.ac.nz)  \n1 Department of Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand  \n2 Mercury Energy, Auckland, New Zealand  \n1 3  \n1 Introduction  \nAccurate electricity demand forecasting is a fundamental component of power system planning and operation. Reliable forecasts support generation scheduling, network investment decisions, congestion management, and the secure integration of renewable energy resources within smart grid environments. Electricity demand is inherently influenced by a range of exogenous drivers, among which weather-related factors such as temperature, seasonal patterns, and climatic variability play a dominant role. Numerous studies have documented the strong and often nonlinear relationship between electricity consumption and weather conditions, highlighting the importance of incorporating meteorological information into demand forecasting models (Bessec & Fouquau, 2008; Mirasgedis et al., 2006; Pardo et al., 2002) .  \nBeyond electricity demand forecasting, a substantial body of literature has examined electricity price forecasting across short-, medium-, and long-term horizons, emphasising the importance of robust and flexible modelling frameworks for energy system planning and market operations (Ghelasi & Ziel, 2025; Kapoor et al., 2025 ; Nowotarski & Weron, 2018; Ziel & Weron, 2018) . These studies highlight key characteristics of electricity systems, including strong temporal dependence, nonlinear dynamics, and sensitivity to exogenous drivers such as weather and market conditions. Although this study focuses on electricity demand rather than prices, insights from this literature reinforce the need for forecasting models that can effectively integrate higher-frequency information and accommodate complex temporal structures.  \nTraditional approaches to electricity demand forecasting have largely relied on classical time-series techniques, including ARIMA and SARIMA models, as well as exp","cbCaicYkPyB7WFW5","https://ap.wps.com/l/cbCaicYkPyB7WFW5","pdf",2422791,"English","# Introduction\n## Weather-driven electricity demand and forecasting objectives\n## Related work: electricity price forecasting and modelling needs\n## Traditional time-series baselines and limitations\n## Mixed-frequency econometric frameworks (MIDAS)\n## Machine learning approaches for electricity demand forecasting","[{\"question\":\"How does the study integrate weather data with electricity generation information?\",\"answer\":\"It uses mixed-frequency modelling to combine daily weather variables with quarterly electricity generation, treated as a demand proxy for New Zealand.\"},{\"question\":\"Which modelling family performs best, and under what evaluation window?\",\"answer\":\"Autoregressive mixed-frequency models deliver strong forecasting performance, especially over shorter recent evaluation windows.\"},{\"question\":\"Why do machine learning-based mixed-frequency models show mixed results?\",\"answer\":\"Their performance is mixed, likely due to challenges from data dimensionality and limited sample size.\"}]","Advanced Theory and Applications in Time Series Analysis - Smart grid electricity demand forecasting using weather-based MIDAS and machine learning models: the case of New Zealand | PDF",1789642874,101]