Pub Date : 2022-11-09DOI: 10.1109/CONCAPAN48024.2022.9997752
Vicente Alonso Navarro Valencia, J. Sánchez-Galán
One pillar of our society is the use of electricity as an engine of development, Short-Term Load Forecasting (STLF) contributes to the resilience and security of electrical supply, by predicting the amount of electricity that should be generated in the near future. Humanity is currently moving from an energy mix based on fossil fuels to a sustainable energy mix, a green one. One challenge of this shift is to forecast, as accurately as possible, the amount of energy load at any moment. This study compares STLF performed by state-of-the-art Neural Network and SARIMA model. First, demand is predicted with SARIMA model and then with a neural network with attention, in this occasion, the Temporal Fusion Transformer (TFT), next, both techniques are compared. The results show that SARIMA is suitable for STLF, with average performance metric values of MAPE and RMSE, of 0.064 and 101.4 MWh, respectively; when use TFT, prediction accuracy increases with a MAPE of 0.044, and RMSE of 69.2 MWh. This research is presented as a review of the state-of-the-technique and thus establishes a baseline that can be used to forecast National Energy Load in the Republic of Panama.
{"title":"Use of Attention-Based Neural Networks to Short-Term Load Forecasting in the Republic of Panama","authors":"Vicente Alonso Navarro Valencia, J. Sánchez-Galán","doi":"10.1109/CONCAPAN48024.2022.9997752","DOIUrl":"https://doi.org/10.1109/CONCAPAN48024.2022.9997752","url":null,"abstract":"One pillar of our society is the use of electricity as an engine of development, Short-Term Load Forecasting (STLF) contributes to the resilience and security of electrical supply, by predicting the amount of electricity that should be generated in the near future. Humanity is currently moving from an energy mix based on fossil fuels to a sustainable energy mix, a green one. One challenge of this shift is to forecast, as accurately as possible, the amount of energy load at any moment. This study compares STLF performed by state-of-the-art Neural Network and SARIMA model. First, demand is predicted with SARIMA model and then with a neural network with attention, in this occasion, the Temporal Fusion Transformer (TFT), next, both techniques are compared. The results show that SARIMA is suitable for STLF, with average performance metric values of MAPE and RMSE, of 0.064 and 101.4 MWh, respectively; when use TFT, prediction accuracy increases with a MAPE of 0.044, and RMSE of 69.2 MWh. This research is presented as a review of the state-of-the-technique and thus establishes a baseline that can be used to forecast National Energy Load in the Republic of Panama.","PeriodicalId":138415,"journal":{"name":"2022 IEEE 40th Central America and Panama Convention (CONCAPAN)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126268619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-09DOI: 10.1109/CONCAPAN48024.2022.9997671
Omar Rivera-Caballero, Alberto Cogley, M. Rios, Jenifer González, Carlos Boya-Lara
Load forecasting is an essential task for the use of technologies such as energy storage systems and distributed energy resources in modern distribution networks. However, these technologies can increase the complexity of the operation of the distribution system due to the variability of its operation. Therefore, accurate load forecasting is necessary, and this will require the use of all available data held by the utility at all voltage levels. In this sense, a hierarchical structure is created in distribution systems, where smart meters allow obtaining granular data. In this paper, we present the hierarchical time series approach using different forecasting models to predict the load demand of a primary substation one hour ahead. To evaluate the performance of forecasting models, the Mean Absolute Percentage Error (MAPE) indicator is used. In this case, the bottom-up approach is used to forecast at the top level. The forecast results reveal that the hierarchical structure provides better performance with the forecast models employed.
{"title":"Hierarchical Forecasting of Load Demand With Smart Meter Data for Distribution Networks","authors":"Omar Rivera-Caballero, Alberto Cogley, M. Rios, Jenifer González, Carlos Boya-Lara","doi":"10.1109/CONCAPAN48024.2022.9997671","DOIUrl":"https://doi.org/10.1109/CONCAPAN48024.2022.9997671","url":null,"abstract":"Load forecasting is an essential task for the use of technologies such as energy storage systems and distributed energy resources in modern distribution networks. However, these technologies can increase the complexity of the operation of the distribution system due to the variability of its operation. Therefore, accurate load forecasting is necessary, and this will require the use of all available data held by the utility at all voltage levels. In this sense, a hierarchical structure is created in distribution systems, where smart meters allow obtaining granular data. In this paper, we present the hierarchical time series approach using different forecasting models to predict the load demand of a primary substation one hour ahead. To evaluate the performance of forecasting models, the Mean Absolute Percentage Error (MAPE) indicator is used. In this case, the bottom-up approach is used to forecast at the top level. The forecast results reveal that the hierarchical structure provides better performance with the forecast models employed.","PeriodicalId":138415,"journal":{"name":"2022 IEEE 40th Central America and Panama Convention (CONCAPAN)","volume":"350 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125630296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-09DOI: 10.1109/CONCAPAN48024.2022.9997632
A. E. Ponce-Martínez, B. B. Hernández-Juárez Posgrado, J. Peña-Antonio, R. Iracheta-Cortez
This paper analyzes the conditions for profitability of an off-shore wind farm located off the coast at the municipality of Salina Cruz, Oaxaca, Mexico. Nearby this site there are onshore farms with the highest annual production in Mexico. For the analysis of the wind resource, two virtual stations which have recorded wind speeds at sea during a total period of 42 years are compared. With the wind resource data, Weibull parameters are calculated and then, two different wind turbines are chosen for analyzing and comparing the annual energy production as well as the wake and Joule losses on site. The selection and performance analysis of medium voltage submarine power cables is carried out through copper loss and voltage drop thresholds. An economic analysis is carried out to demonstrate whether the off-shore wind farm is economically viable by assuming an updated cost analysis for initial investment, operation and maintenance expenses, interest rates as well as electricity sell prices. Finally, a sensitivity analysis is carried out to show different scenarios, such as variable energy production and variable electricity sell prices, where the wind project might be economically viable.
{"title":"Determination of Favourable Conditions for Profitability of an Off-Shore Wind Farm in Mexico","authors":"A. E. Ponce-Martínez, B. B. Hernández-Juárez Posgrado, J. Peña-Antonio, R. Iracheta-Cortez","doi":"10.1109/CONCAPAN48024.2022.9997632","DOIUrl":"https://doi.org/10.1109/CONCAPAN48024.2022.9997632","url":null,"abstract":"This paper analyzes the conditions for profitability of an off-shore wind farm located off the coast at the municipality of Salina Cruz, Oaxaca, Mexico. Nearby this site there are onshore farms with the highest annual production in Mexico. For the analysis of the wind resource, two virtual stations which have recorded wind speeds at sea during a total period of 42 years are compared. With the wind resource data, Weibull parameters are calculated and then, two different wind turbines are chosen for analyzing and comparing the annual energy production as well as the wake and Joule losses on site. The selection and performance analysis of medium voltage submarine power cables is carried out through copper loss and voltage drop thresholds. An economic analysis is carried out to demonstrate whether the off-shore wind farm is economically viable by assuming an updated cost analysis for initial investment, operation and maintenance expenses, interest rates as well as electricity sell prices. Finally, a sensitivity analysis is carried out to show different scenarios, such as variable energy production and variable electricity sell prices, where the wind project might be economically viable.","PeriodicalId":138415,"journal":{"name":"2022 IEEE 40th Central America and Panama Convention (CONCAPAN)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133132838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-09DOI: 10.1109/CONCAPAN48024.2022.9997720
Dania V. Mena, R. Durón, Gracia M. Pineda, J. Bardales, Héctor Villatoro, Gabriela Munguía, Rafael Delgado Elvir, Luis Herrera Maldonado, Javier Hernández, Maloy Portillo, Francisco Torres
Proper territorial data management is critical for territorial planning projects, research, innovation, and the appropriate follow-up to act for the well-being of populations. A multidisciplinary team of professionals established a pilot project named Cortes Data Hub (Centro de Datos de Cortés). It presents several dashboards that show official statistics on the energy sector, mapping the region’s energy demand, data on COVID-19 cases and vaccination rates by municipality or department, and a project using Google Earth that combines post-Eta and Iota observations and a social media campaign for disaster awareness and for the promotion of activities to develop tourism in the San Manuel Municipality. This pilot project shows the importance to observe and monitor various key environmental, health, and socioeconomic data. This will help improve initiatives for local development, disaster prevention and control, and the promotion of the One Health approach. The challenges to overcome are the quality and timing of data. Training more academics, government teams, and decision-makers in the use of new tools for data integration with earth observations are important for the Cortés department’s development.
适当的领土数据管理对于领土规划项目、研究、创新和为人民福祉采取适当后续行动至关重要。一个多学科专业人员小组建立了一个名为科尔特斯数据中心(Centro de Datos de cort)的试点项目。它展示了几个仪表板，显示了能源部门的官方统计数据，绘制了该地区的能源需求图，按城市或部门分列的COVID-19病例和疫苗接种率数据，以及一个利用谷歌Earth的项目，该项目结合了埃塔和伊奥塔之后的观测结果，以及一项提高灾害意识和促进圣曼努埃尔市旅游业发展活动的社交媒体运动。这一试点项目显示了观察和监测各种关键环境、健康和社会经济数据的重要性。这将有助于改进地方发展、灾害预防和控制以及促进“同一个健康”方针的举措。需要克服的挑战是数据的质量和时间。培训更多的学者、政府团队和决策者使用新工具将数据与地球观测相结合，对cortsams部门的发展非常重要。
{"title":"Geospatial, earth observations and statistical data integration in the Cortés department, Honduras","authors":"Dania V. Mena, R. Durón, Gracia M. Pineda, J. Bardales, Héctor Villatoro, Gabriela Munguía, Rafael Delgado Elvir, Luis Herrera Maldonado, Javier Hernández, Maloy Portillo, Francisco Torres","doi":"10.1109/CONCAPAN48024.2022.9997720","DOIUrl":"https://doi.org/10.1109/CONCAPAN48024.2022.9997720","url":null,"abstract":"Proper territorial data management is critical for territorial planning projects, research, innovation, and the appropriate follow-up to act for the well-being of populations. A multidisciplinary team of professionals established a pilot project named Cortes Data Hub (Centro de Datos de Cortés). It presents several dashboards that show official statistics on the energy sector, mapping the region’s energy demand, data on COVID-19 cases and vaccination rates by municipality or department, and a project using Google Earth that combines post-Eta and Iota observations and a social media campaign for disaster awareness and for the promotion of activities to develop tourism in the San Manuel Municipality. This pilot project shows the importance to observe and monitor various key environmental, health, and socioeconomic data. This will help improve initiatives for local development, disaster prevention and control, and the promotion of the One Health approach. The challenges to overcome are the quality and timing of data. Training more academics, government teams, and decision-makers in the use of new tools for data integration with earth observations are important for the Cortés department’s development.","PeriodicalId":138415,"journal":{"name":"2022 IEEE 40th Central America and Panama Convention (CONCAPAN)","volume":"29 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129871875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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