{"title":"约旦电力消费趋势","authors":"Ahmed Banimustafa, Zakaria A. M. Al-Omari","doi":"10.1109/EICEEAI56378.2022.10050498","DOIUrl":null,"url":null,"abstract":"Electricity plays a crucial role in modern civilization. However, despite technological development in electricity generation, storage in large grids is still limited, which necessitates optimizing the electricity generation and distribution to meet demand and reduce waste which can help reduce costs and cut carbon emissions. Predicting the actual electricity consumption can be decisive in achieving these endeavors. This paper aims to investigate the trends of electricity consumption in Jordan based on a historical dataset covering one year. The analysis covers the temporal analysis of electricity consumption over hours, days, weeks, months, and seasons. It also examines the electricity consumption trends in different weather conditions and temperature levels. The dataset used in the analysis was processed using sophisticated data science steps, which involved (1) Data Wrangling, (2) Data Processing, (3) Features Engineering, (4)Trends Analysis, and (5) Results in Evaluation. The trend analysis results achieved in this study were very promising, as it confirms the validity and potential of the data to carry out more predictive forecasting analysis using time series, regression, and machine learning algorithms.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Trends of Electricity Consumption In Jordan\",\"authors\":\"Ahmed Banimustafa, Zakaria A. M. Al-Omari\",\"doi\":\"10.1109/EICEEAI56378.2022.10050498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Electricity plays a crucial role in modern civilization. However, despite technological development in electricity generation, storage in large grids is still limited, which necessitates optimizing the electricity generation and distribution to meet demand and reduce waste which can help reduce costs and cut carbon emissions. Predicting the actual electricity consumption can be decisive in achieving these endeavors. This paper aims to investigate the trends of electricity consumption in Jordan based on a historical dataset covering one year. The analysis covers the temporal analysis of electricity consumption over hours, days, weeks, months, and seasons. It also examines the electricity consumption trends in different weather conditions and temperature levels. The dataset used in the analysis was processed using sophisticated data science steps, which involved (1) Data Wrangling, (2) Data Processing, (3) Features Engineering, (4)Trends Analysis, and (5) Results in Evaluation. The trend analysis results achieved in this study were very promising, as it confirms the validity and potential of the data to carry out more predictive forecasting analysis using time series, regression, and machine learning algorithms.\",\"PeriodicalId\":426838,\"journal\":{\"name\":\"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EICEEAI56378.2022.10050498\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EICEEAI56378.2022.10050498","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Electricity plays a crucial role in modern civilization. However, despite technological development in electricity generation, storage in large grids is still limited, which necessitates optimizing the electricity generation and distribution to meet demand and reduce waste which can help reduce costs and cut carbon emissions. Predicting the actual electricity consumption can be decisive in achieving these endeavors. This paper aims to investigate the trends of electricity consumption in Jordan based on a historical dataset covering one year. The analysis covers the temporal analysis of electricity consumption over hours, days, weeks, months, and seasons. It also examines the electricity consumption trends in different weather conditions and temperature levels. The dataset used in the analysis was processed using sophisticated data science steps, which involved (1) Data Wrangling, (2) Data Processing, (3) Features Engineering, (4)Trends Analysis, and (5) Results in Evaluation. The trend analysis results achieved in this study were very promising, as it confirms the validity and potential of the data to carry out more predictive forecasting analysis using time series, regression, and machine learning algorithms.