Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environment...Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.展开更多
Energy sustains the world, yet fossil fuels, a finite resource, are dwindling. This necessitates a shift towards more sustainable energy sources, such as electricity. Accurate load forecasting is crucial in today’s g...Energy sustains the world, yet fossil fuels, a finite resource, are dwindling. This necessitates a shift towards more sustainable energy sources, such as electricity. Accurate load forecasting is crucial in today’s global energy landscape, as it helps predict various aspects such as production, revenue, consumption, economic conditions, weather impacts, power system utilization, customer demand, and economic growth. For instance, an increase in electricity demand within a country often signifies a boost in industry and production, leading to economic progress and reduced unemployment. This project aims to enhance prediction accuracy through meticulous input filtering, taking into account factors like population growth, planned loads, inflation, and competitive pricing pressures from producers. Despite inherent prediction errors, efforts are made to minimize these discrepancies. This paper introduces a novel combined method for mid-term energy forecasting. To demonstrate its efficacy, real data from the past ten months, collected from subscribers of the Kerman distribution company, was used to forecast energy consumption over the next ten days. The innovative method, which integrates multiple forecasting techniques and robust filters, significantly improves forecasting precision. The following error metrics were recorded for the proposed method: MSE: 0.009, MAE: 0.083, MAPE: 0.776, RMSE: 0.095, AE: 0.013.展开更多
Accurate short-term load forecasting is essential for modern power systems,enabling efficient energy management and supporting grid reliability amid increasing demand and variable weather conditions.This study address...Accurate short-term load forecasting is essential for modern power systems,enabling efficient energy management and supporting grid reliability amid increasing demand and variable weather conditions.This study addresses the challenge of forecasting household electricity consumption by proposing SSRXLR—a novel hybrid method that integrates statistical and machine learning techniques including a sparse,Seasonal Autoregressive Integrated Moving Average Exogenous model,Random Forest,Extreme Gradient Boosting,Long Short-Term Memory,and a Residual Correction step to leverage both linear trends and complex nonlinear relationships.We have analyzed one year of high-resolution(5-minute interval)energy and weather data from a household in Las Vegas,Nevada.Through a rigorous feature selection process,we have identified the four most influential features,i.e.,sea level pressure,temperature,feels-like temperature,and dew point.The proposed method has demonstrated strong prediction performance across multiple metrics.Compared to well-known models,the proposed method achieved a root mean square logarithmic error of 0.043,which surpassed the Random Forest method by 0.066 and the Seasonal Autoregressive Integrated Moving Average Exogenous model by 0.106 in reducing the Root Mean Square Logarithmic Error(RMSLE).The coefficient of determination for the proposed method attained a 0.97 value,outperforming Random Forest(0.92)and the Seasonal Autoregressive Integrated Moving Average Exogenous model(0.67).These results highlight the effectiveness of combining advanced statistical modeling,machine learning,and targeted feature selection for precise short-term load forecasting.The proposed framework offers a scalable solution for smart grid operations,resource planning,and integration of renewable energy in diverse environments.展开更多
基金supported by the Startup Grant(PG18929)awarded to F.Shokoohi.
文摘Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.
文摘Energy sustains the world, yet fossil fuels, a finite resource, are dwindling. This necessitates a shift towards more sustainable energy sources, such as electricity. Accurate load forecasting is crucial in today’s global energy landscape, as it helps predict various aspects such as production, revenue, consumption, economic conditions, weather impacts, power system utilization, customer demand, and economic growth. For instance, an increase in electricity demand within a country often signifies a boost in industry and production, leading to economic progress and reduced unemployment. This project aims to enhance prediction accuracy through meticulous input filtering, taking into account factors like population growth, planned loads, inflation, and competitive pricing pressures from producers. Despite inherent prediction errors, efforts are made to minimize these discrepancies. This paper introduces a novel combined method for mid-term energy forecasting. To demonstrate its efficacy, real data from the past ten months, collected from subscribers of the Kerman distribution company, was used to forecast energy consumption over the next ten days. The innovative method, which integrates multiple forecasting techniques and robust filters, significantly improves forecasting precision. The following error metrics were recorded for the proposed method: MSE: 0.009, MAE: 0.083, MAPE: 0.776, RMSE: 0.095, AE: 0.013.
文摘Accurate short-term load forecasting is essential for modern power systems,enabling efficient energy management and supporting grid reliability amid increasing demand and variable weather conditions.This study addresses the challenge of forecasting household electricity consumption by proposing SSRXLR—a novel hybrid method that integrates statistical and machine learning techniques including a sparse,Seasonal Autoregressive Integrated Moving Average Exogenous model,Random Forest,Extreme Gradient Boosting,Long Short-Term Memory,and a Residual Correction step to leverage both linear trends and complex nonlinear relationships.We have analyzed one year of high-resolution(5-minute interval)energy and weather data from a household in Las Vegas,Nevada.Through a rigorous feature selection process,we have identified the four most influential features,i.e.,sea level pressure,temperature,feels-like temperature,and dew point.The proposed method has demonstrated strong prediction performance across multiple metrics.Compared to well-known models,the proposed method achieved a root mean square logarithmic error of 0.043,which surpassed the Random Forest method by 0.066 and the Seasonal Autoregressive Integrated Moving Average Exogenous model by 0.106 in reducing the Root Mean Square Logarithmic Error(RMSLE).The coefficient of determination for the proposed method attained a 0.97 value,outperforming Random Forest(0.92)and the Seasonal Autoregressive Integrated Moving Average Exogenous model(0.67).These results highlight the effectiveness of combining advanced statistical modeling,machine learning,and targeted feature selection for precise short-term load forecasting.The proposed framework offers a scalable solution for smart grid operations,resource planning,and integration of renewable energy in diverse environments.
