The Experts below are selected from a list of 39585 Experts worldwide ranked by ideXlab platform
D Carmonafernandez - One of the best experts on this subject based on the ideXlab platform.
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monthly electric energy Demand Forecasting with neural networks and fourier series
Energy Conversion and Management, 2008Co-Authors: Eva Gonzalezromera, Miguel A Jaramillomoran, D CarmonafernandezAbstract:Medium-term electric energy Demand Forecasting is a useful tool for grid maintenance planning and market research of electric energy companies. Several methods, such as ARIMA, regression or artificial intelligence, have been usually used to carry out those predictions. Some approaches include weather or economic variables, which strongly influence electric energy Demand. Economic variables usually influence the general series trend, while weather provides a periodic behavior because of its seasonal nature. This work investigates the periodic behavior of the Spanish monthly electric Demand series, obtained by rejecting the trend from the consumption series. A novel hybrid approach is proposed: the periodic behavior is forecasted with a Fourier series while the trend is predicted with a neural network. Satisfactory results have been obtained, with a lower than 2% MAPE, which improve those reached when only neural networks or ARIMA were used for the same purpose.
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monthly electric energy Demand Forecasting based on trend extraction
IEEE Transactions on Power Systems, 2006Co-Authors: Eva Gonzalezromera, Miguel A Jaramillomoran, D CarmonafernandezAbstract:Medium-term electric energy Demand Forecasting is an essential tool for power system planning and operation, mainly in those countries whose power systems operate in a deregulated environment. This paper proposes a novel approach to monthly electric energy Demand time series Forecasting, in which it is split into two new series: the trend and the fluctuation around it. Then two neural networks are trained to forecast them separately. These predictions are added up to obtain an overall Forecasting. Several methods have been tested to find out which of them provides the best performance in the trend extraction. The proposed technique has been applied to the Spanish peninsular monthly electric consumption. The results obtained are better than those reached when only one neural network was used to forecast the original consumption series and also than those obtained with the ARIMA method
Seonghwan Yoon - One of the best experts on this subject based on the ideXlab platform.
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development of an urban energy Demand Forecasting system to support environmentally friendly urban planning
Applied Energy, 2013Co-Authors: Inae Yeo, Seonghwan Yoon, Jurngjae YeeAbstract:Abstract This study proposes a new urban energy Demand Forecasting system that includes the following improvements: (a) a facility planning information database (DB), (b) an energy and planning statistics DB, and (c) an enhancement of the accuracy of the energy calculation method. Each of these improved aspects is involved in energy Demand Forecasting for urban planning. The results from this study are as follows. (1). An Environment and energy Geographical Information System Database (E-GIS DB), which provides the mesh unit facility information, was utilized to allow for the Forecasting and control of urban energy Demands for each unit space. (2). An energy consumption unit figure was connected with an energy simulation to diversify the level of the urban energy consumption sector and the primary energy into hourly information. This figure allows for more accurate Demand Forecasting. (3). Urban facilities were categorized according to energy use characteristics and were modeled to allow for energy Demand forecasts. (4). The energy Demand was considered in an urban climate during summer with the characteristics of the heating methods that are suitable for domestic circumstances. Thus, a separate algorithm was suggested for a cooling period and a heating/intermission period to enhance the accuracy of the Demand forecasts. (5). The performance of this energy Demand Forecasting system was validated, such that excessively high or low calculated values can be modified from the current method in a ‘planned city’ while the urban energy Demand can be forecasted relatively correct and in detail with differences of a factor of 0.20–0.44 for the cooling period in the ‘existing city’. (6). The proposed urban energy Demand Forecasting system was constructed as EnerISS Solver which is an automated module. This module drastically reduced time consuming for predicting energy Demand and urban climate to respond for the urban energy planning subjects’ needs immediately.
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development of an urban energy Demand Forecasting system to support environmentally friendly urban planning
Applied Energy, 2013Co-Authors: Seonghwan YoonAbstract:This study proposes a new urban energy Demand Forecasting system that includes the following improvements: (a) a facility planning information database (DB), (b) an energy and planning statistics DB, and (c) an enhancement of the accuracy of the energy calculation method. Each of these improved aspects is involved in energy Demand Forecasting for urban planning. The results from this study are as follows.
Eva Gonzalezromera - One of the best experts on this subject based on the ideXlab platform.
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monthly electric energy Demand Forecasting with neural networks and fourier series
Energy Conversion and Management, 2008Co-Authors: Eva Gonzalezromera, Miguel A Jaramillomoran, D CarmonafernandezAbstract:Medium-term electric energy Demand Forecasting is a useful tool for grid maintenance planning and market research of electric energy companies. Several methods, such as ARIMA, regression or artificial intelligence, have been usually used to carry out those predictions. Some approaches include weather or economic variables, which strongly influence electric energy Demand. Economic variables usually influence the general series trend, while weather provides a periodic behavior because of its seasonal nature. This work investigates the periodic behavior of the Spanish monthly electric Demand series, obtained by rejecting the trend from the consumption series. A novel hybrid approach is proposed: the periodic behavior is forecasted with a Fourier series while the trend is predicted with a neural network. Satisfactory results have been obtained, with a lower than 2% MAPE, which improve those reached when only neural networks or ARIMA were used for the same purpose.
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monthly electric energy Demand Forecasting based on trend extraction
IEEE Transactions on Power Systems, 2006Co-Authors: Eva Gonzalezromera, Miguel A Jaramillomoran, D CarmonafernandezAbstract:Medium-term electric energy Demand Forecasting is an essential tool for power system planning and operation, mainly in those countries whose power systems operate in a deregulated environment. This paper proposes a novel approach to monthly electric energy Demand time series Forecasting, in which it is split into two new series: the trend and the fluctuation around it. Then two neural networks are trained to forecast them separately. These predictions are added up to obtain an overall Forecasting. Several methods have been tested to find out which of them provides the best performance in the trend extraction. The proposed technique has been applied to the Spanish peninsular monthly electric consumption. The results obtained are better than those reached when only one neural network was used to forecast the original consumption series and also than those obtained with the ARIMA method
Nancy M Heddle - One of the best experts on this subject based on the ideXlab platform.
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a decision integration strategy for short term Demand Forecasting and ordering for red blood cell components
Operations research for health care, 2021Co-Authors: Fei Chiang, Douglas G Down, Nancy M HeddleAbstract:Abstract Blood transfusion is one of the most crucial and commonly administered therapeutics worldwide. The need for more accurate and efficient ways to manage blood Demand and supply is an increasing concern. Building a technology-based, robust blood Demand and supply chain that can achieve the goals of reducing ordering frequency, inventory level, wastage and shortage, while maintaining the safety of blood usage, is essential in modern healthcare systems. In this study, we summarize the key challenges in current Demand and supply management for red blood cells (RBCs). We combine ideas from statistical time series modeling, machine learning, and operations research in developing an ordering decision strategy for RBCs, through integrating a hybrid Demand Forecasting model using clinical predictors and a data-driven multi-period inventory problem considering inventory and reorder constraints. We have applied the integrated ordering strategy to the blood inventory management system in Hamilton, Ontario using a large clinical database from 2008 to 2018. The proposed hybrid Demand Forecasting model provides robust and accurate predictions, and identifies important clinical predictors for short-term RBC Demand Forecasting. Compared with the actual historical data, our integrated ordering strategy reduces the inventory level by 40% and decreases the ordering frequency by 60%, with low incidence of shortages and wastage due to expiration. If implemented successfully, our proposed strategy can achieve significant cost savings for healthcare systems and blood suppliers. The proposed ordering strategy is generalizable to other blood products or even other perishable products.
Jurngjae Yee - One of the best experts on this subject based on the ideXlab platform.
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development of an urban energy Demand Forecasting system to support environmentally friendly urban planning
Applied Energy, 2013Co-Authors: Inae Yeo, Seonghwan Yoon, Jurngjae YeeAbstract:Abstract This study proposes a new urban energy Demand Forecasting system that includes the following improvements: (a) a facility planning information database (DB), (b) an energy and planning statistics DB, and (c) an enhancement of the accuracy of the energy calculation method. Each of these improved aspects is involved in energy Demand Forecasting for urban planning. The results from this study are as follows. (1). An Environment and energy Geographical Information System Database (E-GIS DB), which provides the mesh unit facility information, was utilized to allow for the Forecasting and control of urban energy Demands for each unit space. (2). An energy consumption unit figure was connected with an energy simulation to diversify the level of the urban energy consumption sector and the primary energy into hourly information. This figure allows for more accurate Demand Forecasting. (3). Urban facilities were categorized according to energy use characteristics and were modeled to allow for energy Demand forecasts. (4). The energy Demand was considered in an urban climate during summer with the characteristics of the heating methods that are suitable for domestic circumstances. Thus, a separate algorithm was suggested for a cooling period and a heating/intermission period to enhance the accuracy of the Demand forecasts. (5). The performance of this energy Demand Forecasting system was validated, such that excessively high or low calculated values can be modified from the current method in a ‘planned city’ while the urban energy Demand can be forecasted relatively correct and in detail with differences of a factor of 0.20–0.44 for the cooling period in the ‘existing city’. (6). The proposed urban energy Demand Forecasting system was constructed as EnerISS Solver which is an automated module. This module drastically reduced time consuming for predicting energy Demand and urban climate to respond for the urban energy planning subjects’ needs immediately.