The Experts below are selected from a list of 360 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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forecasting of the Electric Energy demand trend and monthly fluctuation with neural networks
Computers & Industrial Engineering, 2007Co-Authors: Eva Gonzalezromera, Miguel A Jaramillomoran, D CarmonafernandezAbstract:Electric Energy demand forecasting is a fundamental tool for production and distribution companies because it provides them with a prediction of the market demand of Electric Energy. Two kinds of forecasting may be performed: short term and medium to long term. This work is focused on monthly prediction, which is useful for the maintenance planning of grids and as market research for Electricity producers and resellers. The timed series of monthly Electric Energy demands presents a rising tendency due to the influence of economic and technological evolution on the Electric market. Embedded in this general trend is a fluctuation caused by the difference in demand from month to month. This paper proposes the extraction of that trend to perform separate predictions of both tendency and fluctuation with neural networks, which will be summed up to obtain the series forecasting. A Mean Absolute Percentage Error (MAPE) of about 2% has been obtained.
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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
D Shmilovitz - One of the best experts on this subject based on the ideXlab platform.
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a review and insights on poynting vector theory and periodic averaged Electric Energy transport theories
Renewable & Sustainable Energy Reviews, 2015Co-Authors: N Calamaro, Yuval Beck, D ShmilovitzAbstract:Electric Energy transport theory is one of the key factors in smart grid development. Electric Energy and power transport theories enable the development of advanced Energy metering, improve fault-location algorithms, Electric network diagnostics algorithms, power conditioning of active filters and of smart grids, Energy storage management at Energy reservoirs, and new Energy forecasting analytical algorithms. In this paper, the periodic averaged theories, including the periodic averaged formulation of Poynting vector theory, are presented. The presentation is conducted in a unique comparative formalism and approach, enabling the demonstration of equivalence between the various theories in general and the equivalence of some elements in the decomposition of the Electrical expressions for the powers in particular. The paper shows the equivalence of the modern theory of Currents׳ Physical Components to the Conservative Power Theory, and its formulation, for being applicable to various grid monitoring and Energy control applications.
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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forecasting of the Electric Energy demand trend and monthly fluctuation with neural networks
Computers & Industrial Engineering, 2007Co-Authors: Eva Gonzalezromera, Miguel A Jaramillomoran, D CarmonafernandezAbstract:Electric Energy demand forecasting is a fundamental tool for production and distribution companies because it provides them with a prediction of the market demand of Electric Energy. Two kinds of forecasting may be performed: short term and medium to long term. This work is focused on monthly prediction, which is useful for the maintenance planning of grids and as market research for Electricity producers and resellers. The timed series of monthly Electric Energy demands presents a rising tendency due to the influence of economic and technological evolution on the Electric market. Embedded in this general trend is a fluctuation caused by the difference in demand from month to month. This paper proposes the extraction of that trend to perform separate predictions of both tendency and fluctuation with neural networks, which will be summed up to obtain the series forecasting. A Mean Absolute Percentage Error (MAPE) of about 2% has been obtained.
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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
Andrzej Lewicki - One of the best experts on this subject based on the ideXlab platform.
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short term forecast of generation of Electric Energy in photovoltaic systems
Renewable & Sustainable Energy Reviews, 2018Co-Authors: Artur Bugala, Maciej Zaborowicz, Piotr Boniecki, Damian Janczak, Krzysztof Koszela, Wojciech Czekala, Andrzej LewickiAbstract:Abstract The paper presents the use of classical statistical methods and methods based on neural modeling in short-term forecasting of Electric Energy from photovoltaic conversion. A detailed analysis of the input data measured in central Poland (Poznan, 52°25′ N, 16°56′ E) showed that some variables like air pressure and the length of the day are statistically insignificant. The values of kurtosis, skewness and results of applied tests, to check the normality of the distribution of dependent variable in the form of daily Electricity production, indicate that the linear regression models should not be the only method in forecast process. The result of neural modeling using implemented network designer is RBF 6: 6-5-1: 1 model with quality test approximately 93% and the RMS error of 0.02%. The input parameters necessary for the operation of proposed ANN model are: number of sunny hours, length of the day, air pressure, maximum air temperature, daily insolation and cloudiness.
Mats Leijon - One of the best experts on this subject based on the ideXlab platform.
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On the physics of power, Energy and economics of renewable Electric Energy sources - Part I
Renewable Energy, 2010Co-Authors: Mats Leijon, Annika Skoglund, Rafael Waters, Alf Mikael Rehn, Marcus LindahlAbstract:Renewable Energy Technologies (RETs) are often recognized as less competitive than traditional Electric Energy conversion systems. Obstacles with renewable Electric Energy conversion systems are often referred to the intermittency of the Energy sources [1] and the relatively high maintenance cost. However, due to an intensified discourse on climate change and its effects, it has from a societal point of view, become more desirable to adopt and install CO2 neutral power plants. Even if this has increased the competitiveness of RETs in a political sense, the new goals for RET installations must also be met with economical viability. We propose that the direction of technical development, as well as the chosen technology in new installations, should not primarily be determined by policies, but by the basic physical properties of the Energy source and the associated potential for inexpensive Energy production. This potential is the basic entity that drives the payback of the investment of a specific RET power plant. With regard to this, we argue that the total Electric Energy conversion system must be considered if effective power production is to be achieved, with focus on the possible number of full loading hours and the Degree of Utilization [2]. This will increase the cost efficiency and economical competitiveness of RET investments, and could enhance faster diffusion of new innovations and installations without over-optimistic subsidies. This paper elaborates on the overall problem of the economy of renewable Electric Energy conversion systems by studying the interface between physics, engineering and economy reported for RET power plants in different scientific publications. The core objective is to show the practical use of the Degree of Utilization and how the concept is crucial for the design and economical optimization disregarding subsidies. The results clearly indicate that the future political regulative frameworks should consider the choice of renewable Energy source since this strongly affects the economical output from the RET power plants.
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Direct Electric Energy conversion system for Energy conversion from marine currents
Proceedings of the Institution of Mechanical Engineers Part A: Journal of Power and Energy, 2007Co-Authors: Mats Leijon, Karin NilssonAbstract:AbstractDirect Electric Energy conversion without input power regulating system or gearboxes may offer a simpler and thus low cost way of converting the Energy of moving water. The proposed electri...
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multiphysics simulation of wave Energy to Electric Energy conversion by permanent magnet linear generator
IEEE Transactions on Energy Conversion, 2005Co-Authors: Mats Leijon, Hans Bernhoff, Olov Agren, Jan Isberg, Jan Sundberg, Marcus Berg, Karl Erik Karlsson, Arne WolfbrandtAbstract:The possibility to use three-phase permanent magnet linear generators to convert sea wave Energy into Electric Energy is investigated by multiphysics simulations. The results show a possibility, which needs to be further verified by experimental tests, for a future step toward a sustainable Electric power production from ocean waves by using direct conversion. The results suggest that wave Energy can have an impact on tomorrow's new sustainable Electricity production, not only for large units, but also for units ranging down to 10 kW. This gives wave power a larger economical potential than previously estimated. The study demonstrates the feasibility of computer simulations to give a broad, and in several aspects a detailed, understanding of the Energy conversion. The simulation results also give a useful starting point for future experimental work.