[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119020-en":3,"doc-seo-119020-105":30,"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":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},119020,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Hybrid Optimisation and Machine Learning Models for Wind & Solar Data Prediction","Energy demand growth is driving massive fossil-fuel consumption and harming the environment, making renewable energy infrastructure a critical alternative. Integrating higher shares of renewables into the electricity mix requires understanding the long-term variability of wind and solar and delivering accurate forecasts. Reliable prediction supports better operational decisions for microgrids and network integration. This work proposes machine learning methods compared with a hybrid approach combining learning with optimisation.","Hybrid Optimisation and Machine Learning Models for Wind & Solar Data Prediction  \nYahia Amoura 1 ,3[0000−0002−8811−0823] , Santiago Torres3[0000−0002−3155−5039] , Jos´e Lima 1 ,2[0000−0001−7902−1207] , Ana I. Pereira 1[0000−0003−3803−2043]  \n1 Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Polit´ecnico de Bragan¸ca, Bragan¸ca, Portugal  \n2 INESC TEC-INESC Technology and Science, Porto, Portugal  \n3 University of Laguna, Spain  \nEmail: {yahia,jllima,[apereira](apereira}@ipb.pt)[}](apereira}@ipb.pt)[@ipb.pt](apereira}@ipb.pt), storres@ull.es  \nAbstract. The exponential growth in energy demand is leading to massive energy consumption from fossil resources causing a negative effects for the environment. It is essential to promote sustainable solutions based on renewable energies infrastructures such as microgrids integrated to the existing network or as stand alone solution. Moreover, the major focus of today is being able to integrate a higher percentages of renewable electricity into the energy mix. The variability of wind and solar energy requires knowing the relevant long-term patterns for developing better procedures and capabilities to facilitate integration to the network. Precise prediction is essential for an adequate use of these renewable sources.  \nThis article proposes machine learning approaches compared to an hybrid method, based on the combination of machine learning with optimisation approaches. The results show the improvement in the accuracy of the machine learning models results once the optimisation approach is used.  \nKeywords: Renewable Energy · Forecasting · Machine Learning · Optimisation · Wind Speed · Solar Irradiation.  \n1 Introduction  \nA considerable proportion of worldwide and especially domestic demand on energy is satisfied by the burning of the fossil fuels [1] . It is broadly recognised that the combustion of fossil fuels such as oil, coal and natural gas emits a great quantity of greenhouse gases (GHG) into the atmosphere, which has very negative effects on the environment [2] . The generation of greener or carbon-free energy can be obtained by exploiting renewable energy sources (RES) such as wind and solar, that have already been developed to satisfy the growing energy needs of the planet [3], trying to minimize the rising product costs. The liberalization of the energy market for electricity, coupled with the growing need for sustainable energy, has pushed policy makers and investors to make greater use of RES to meet the global energy challenge [4] . The injection of these RES into the energy  \n2 Amoura et al.  \nmix needs a large backup generation capacity in order to remove the intermittent nature of such sources. This intermittency causes several problems on the quality of observed power output: voltage fluctuation, power system transientsand harmonics, reactive power, electromagnetic interference, switching actions, synchronisation, low power factor, etc. Given technological and economic limitations in current energy storage techniques, it will be difficult to accomplish the current mandates for renewable energy integration.  \nMoreover, the power generated by wind turbine and photovoltaic systems is strongly influenced by local weather conditions, such as climate temperature, wind velocity, precipitation, relative humidity, solar irradiation and their fluctuations. Consequently, wind and solar energy production is usually rather complex to supervise and forecast, which has rendered the incorporation of both wind and solar energy into electrical networks as a challenge [5] .  \nTo address the above challenge, it is crucial to enhance the forecasting capability of wind velocity and solar irradiation in order to mitigate the degree of confidence regarding the potential output of sustainable energy in any operating conditions of the power grid [6] . Given the relationship that exists within solar irradiance and photovoltaic, and the relationship that occurs betw","cbCaipSk1nOUStwO","https://ap.wps.com/l/cbCaipSk1nOUStwO","pdf",1339377,1,22,"English","en",105,"# Introduction\n## Forecasting need and renewable integration\n## Time-scale classification for wind and solar prediction\n## Existing forecasting approaches: physics, data-driven, and hybrid\n## Physics-based methods\n## Data-dependent and machine learning models\n## Hybrid optimisation and model motivation","[{\"question\":\"Why is forecasting wind speed and solar irradiation important for renewable integration?\",\"answer\":\"Forecasting enables mitigation of uncertainty in renewable power output under different grid operating conditions, supporting more reliable integration.\"},{\"question\":\"How does the document categorize prediction horizons for wind and solar?\",\"answer\":\"Wind speed is grouped into four intervals (very short to long term), while solar irradiation is separated into four temporal categories (minutes to multiple-day horizons).\"},{\"question\":\"What is the main idea behind the proposed hybrid approach?\",\"answer\":\"The approach combines machine learning models with optimisation methods, and the results indicate improved prediction accuracy when optimisation is used.\"}]","Hybrid Optimisation and Machine Learning Models for Wind & Solar Data Prediction | 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is forecasting wind speed and solar irradiation important for renewable integration?","Question",{"text":76,"@type":77},"Forecasting enables mitigation of uncertainty in renewable power output under different grid operating conditions, supporting more reliable integration.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the document categorize prediction horizons for wind and solar?",{"text":81,"@type":77},"Wind speed is grouped into four intervals (very short to long term), while solar irradiation is separated into four temporal categories (minutes to multiple-day horizons).",{"name":83,"@type":74,"acceptedAnswer":84},"What is the main idea behind the proposed hybrid approach?",{"text":85,"@type":77},"The approach combines machine learning models with optimisation methods, and the results indicate improved prediction accuracy when optimisation is 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