Hungary＇s accession to the European Union in 2004 marked the start of a new era. The completion of legal harmonization brought in new emission standards and thresholds, and this was followed by EU-assisted projects t...Hungary＇s accession to the European Union in 2004 marked the start of a new era. The completion of legal harmonization brought in new emission standards and thresholds, and this was followed by EU-assisted projects to build and reconstruct wastewater treatment plants. The utilization of sludge after it leaves the wastewater treatment plant is now a solely private-sector operation. There are new political priorities： increasing state involvement, buying out of major--formerly privatized--service providers, reintegration of isolated service providers, legally-imposed reduction of public utility charges. As a result, a demand has arisen for a greater central, i.e. government role in sludge utilization.展开更多
The rapid phase-in of electric vehicles(EV)will cause unprecedented issues with managing the supply of electricity and charging stations.It is in the interest of utility providers and everyday consumers to be able to ...The rapid phase-in of electric vehicles(EV)will cause unprecedented issues with managing the supply of electricity and charging stations.It is in the interest of utility providers and everyday consumers to be able to plan for peak charging times,and related congestion.While past work has been done for localized,short-term forecasting,it has not included longer term forecasting,or considered the relationships between multiple stations.Importantly,past work has also not offered a framework for dataset construction and evaluated different dataset features.We propose a methodology to forecast demand at public EV charging stations,and use it to explore the potential of data-driven models to predict demand up to one week in advance.Our strategy includes selecting parameters for formatting a dataset given a list of charging events,a way to consider flexible prediction horizons,and deployment of deep and supervised learning-based models.To the best of our knowledge,ours is the first study to propose machine learning to forecast medium-term public EV charging demand,to exploit weather and other features at public charging stations,and to forecast demand at multiple stations and the entire network.We validated our approach using data from eleven stations over three years from Scotland,UK.Our method outperforms the benchmark time series method,and predicts network demand with a symmetric mean absolute percentage error(SMAPE)of 5.9%and a mean absolute error(MAE)of 124.7 kWh,or less than twelve percent of average daily demand.展开更多
文摘Hungary＇s accession to the European Union in 2004 marked the start of a new era. The completion of legal harmonization brought in new emission standards and thresholds, and this was followed by EU-assisted projects to build and reconstruct wastewater treatment plants. The utilization of sludge after it leaves the wastewater treatment plant is now a solely private-sector operation. There are new political priorities： increasing state involvement, buying out of major--formerly privatized--service providers, reintegration of isolated service providers, legally-imposed reduction of public utility charges. As a result, a demand has arisen for a greater central, i.e. government role in sludge utilization.
文摘The rapid phase-in of electric vehicles(EV)will cause unprecedented issues with managing the supply of electricity and charging stations.It is in the interest of utility providers and everyday consumers to be able to plan for peak charging times,and related congestion.While past work has been done for localized,short-term forecasting,it has not included longer term forecasting,or considered the relationships between multiple stations.Importantly,past work has also not offered a framework for dataset construction and evaluated different dataset features.We propose a methodology to forecast demand at public EV charging stations,and use it to explore the potential of data-driven models to predict demand up to one week in advance.Our strategy includes selecting parameters for formatting a dataset given a list of charging events,a way to consider flexible prediction horizons,and deployment of deep and supervised learning-based models.To the best of our knowledge,ours is the first study to propose machine learning to forecast medium-term public EV charging demand,to exploit weather and other features at public charging stations,and to forecast demand at multiple stations and the entire network.We validated our approach using data from eleven stations over three years from Scotland,UK.Our method outperforms the benchmark time series method,and predicts network demand with a symmetric mean absolute percentage error(SMAPE)of 5.9%and a mean absolute error(MAE)of 124.7 kWh,or less than twelve percent of average daily demand.
