{"title":"概率负荷预测中的空间天气、社会经济和政治风险","authors":"Monika Zimmermann, Florian Ziel","doi":"arxiv-2408.00507","DOIUrl":null,"url":null,"abstract":"Accurate forecasts of the impact of spatial weather and pan-European\nsocio-economic and political risks on hourly electricity demand for the\nmid-term horizon are crucial for strategic decision-making amidst the inherent\nuncertainty. Most importantly, these forecasts are essential for the\noperational management of power plants, ensuring supply security and grid\nstability, and in guiding energy trading and investment decisions. The primary\nchallenge for this forecasting task lies in disentangling the multifaceted\ndrivers of load, which include national deterministic (daily, weekly, annual,\nand holiday patterns) and national stochastic weather and autoregressive\neffects. Additionally, transnational stochastic socio-economic and political\neffects add further complexity, in particular, due to their non-stationarity.\nTo address this challenge, we present an interpretable probabilistic mid-term\nforecasting model for the hourly load that captures, besides all deterministic\neffects, the various uncertainties in load. This model recognizes transnational\ndependencies across 24 European countries, with multivariate modeled\nsocio-economic and political states and cross-country dependent forecasting.\nBuilt from interpretable Generalized Additive Models (GAMs), the model enables\nan analysis of the transmission of each incorporated effect to the\nhour-specific load. Our findings highlight the vulnerability of countries\nreliant on electric heating under extreme weather scenarios. This emphasizes\nthe need for high-resolution forecasting of weather effects on pan-European\nelectricity consumption especially in anticipation of widespread electric\nheating adoption.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"21 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatial Weather, Socio-Economic and Political Risks in Probabilistic Load Forecasting\",\"authors\":\"Monika Zimmermann, Florian Ziel\",\"doi\":\"arxiv-2408.00507\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate forecasts of the impact of spatial weather and pan-European\\nsocio-economic and political risks on hourly electricity demand for the\\nmid-term horizon are crucial for strategic decision-making amidst the inherent\\nuncertainty. Most importantly, these forecasts are essential for the\\noperational management of power plants, ensuring supply security and grid\\nstability, and in guiding energy trading and investment decisions. The primary\\nchallenge for this forecasting task lies in disentangling the multifaceted\\ndrivers of load, which include national deterministic (daily, weekly, annual,\\nand holiday patterns) and national stochastic weather and autoregressive\\neffects. Additionally, transnational stochastic socio-economic and political\\neffects add further complexity, in particular, due to their non-stationarity.\\nTo address this challenge, we present an interpretable probabilistic mid-term\\nforecasting model for the hourly load that captures, besides all deterministic\\neffects, the various uncertainties in load. This model recognizes transnational\\ndependencies across 24 European countries, with multivariate modeled\\nsocio-economic and political states and cross-country dependent forecasting.\\nBuilt from interpretable Generalized Additive Models (GAMs), the model enables\\nan analysis of the transmission of each incorporated effect to the\\nhour-specific load. Our findings highlight the vulnerability of countries\\nreliant on electric heating under extreme weather scenarios. This emphasizes\\nthe need for high-resolution forecasting of weather effects on pan-European\\nelectricity consumption especially in anticipation of widespread electric\\nheating adoption.\",\"PeriodicalId\":501273,\"journal\":{\"name\":\"arXiv - ECON - General Economics\",\"volume\":\"21 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - ECON - General Economics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.00507\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - General Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.00507","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Spatial Weather, Socio-Economic and Political Risks in Probabilistic Load Forecasting
Accurate forecasts of the impact of spatial weather and pan-European
socio-economic and political risks on hourly electricity demand for the
mid-term horizon are crucial for strategic decision-making amidst the inherent
uncertainty. Most importantly, these forecasts are essential for the
operational management of power plants, ensuring supply security and grid
stability, and in guiding energy trading and investment decisions. The primary
challenge for this forecasting task lies in disentangling the multifaceted
drivers of load, which include national deterministic (daily, weekly, annual,
and holiday patterns) and national stochastic weather and autoregressive
effects. Additionally, transnational stochastic socio-economic and political
effects add further complexity, in particular, due to their non-stationarity.
To address this challenge, we present an interpretable probabilistic mid-term
forecasting model for the hourly load that captures, besides all deterministic
effects, the various uncertainties in load. This model recognizes transnational
dependencies across 24 European countries, with multivariate modeled
socio-economic and political states and cross-country dependent forecasting.
Built from interpretable Generalized Additive Models (GAMs), the model enables
an analysis of the transmission of each incorporated effect to the
hour-specific load. Our findings highlight the vulnerability of countries
reliant on electric heating under extreme weather scenarios. This emphasizes
the need for high-resolution forecasting of weather effects on pan-European
electricity consumption especially in anticipation of widespread electric
heating adoption.