{"title":"基于蒙特卡罗模拟的可再生资源和智能家居数据建模","authors":"Bahman Naghibi","doi":"10.1109/EEEIC.2019.8783303","DOIUrl":null,"url":null,"abstract":"This paper introduces the use of Monte Carlo simulations (MCSs) for modeling stochastic behavior of wind speed, irradiance, temperature, load and electricity rate (ER) as well as the availability of PEV. Two methods are introduced. Probability distributions and their parameters are described in the first method which can be use in the future researches. Second method is introduced for MCS to consider the correlation between different databases and the correlation of each interval value with their prior interval value. Recommendations are provided for the first method in the future studies.","PeriodicalId":422977,"journal":{"name":"2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data Modeling for Renewable Resources and Smart Home using Monte Carlo Simulations\",\"authors\":\"Bahman Naghibi\",\"doi\":\"10.1109/EEEIC.2019.8783303\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces the use of Monte Carlo simulations (MCSs) for modeling stochastic behavior of wind speed, irradiance, temperature, load and electricity rate (ER) as well as the availability of PEV. Two methods are introduced. Probability distributions and their parameters are described in the first method which can be use in the future researches. Second method is introduced for MCS to consider the correlation between different databases and the correlation of each interval value with their prior interval value. Recommendations are provided for the first method in the future studies.\",\"PeriodicalId\":422977,\"journal\":{\"name\":\"2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EEEIC.2019.8783303\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EEEIC.2019.8783303","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data Modeling for Renewable Resources and Smart Home using Monte Carlo Simulations
This paper introduces the use of Monte Carlo simulations (MCSs) for modeling stochastic behavior of wind speed, irradiance, temperature, load and electricity rate (ER) as well as the availability of PEV. Two methods are introduced. Probability distributions and their parameters are described in the first method which can be use in the future researches. Second method is introduced for MCS to consider the correlation between different databases and the correlation of each interval value with their prior interval value. Recommendations are provided for the first method in the future studies.