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Inhaltsangabe
Electric load forecasting is an important research field in electric power industry. It plays a crucial role in solving a wide range of tasks of short-term planning and operating control of electric power system operating modes. Load forecasting is carried out in different time spans. Load forecasting within a current day – operating forecasting; one-day-week-month-ahead load forecasting – short-term load forecasting; one-month-quarter-year-ahead load forecasting – long-term load forecasting. So far a great number of both conventional and non-conventional electric load forecasting methods and models have been developed. The work presents research results of electric load forecasting for electrical power systems using artificial neural networks and fuzzy logic as one of the most advanced and perspective directions of solving this task. A theoretical approach to the issues discussed is combined with the data of experimental studies implemented with application of load curves of regional electrical power systems. The book is addressed to specialists and researchers concerned with operational control modes of electric power systems.
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Electric load forecasting is an important research field in electric power industry. It plays a crucial role in solving a wide range of tasks of short-term planning and operating control of electric power system operating modes. Load forecasting is carried out in different time spans. Load forecasting within a current day - operating forecasting; one-day-week-month-ahead load forecasting - short-term load forecasting; one-month-quarter-year-ahead load forecasting - long-term load forecasting. So far a great number of both conventional and non-conventional electric load forecasting methods and models have been developed. The work presents research results of electric load forecasting for electrical power systems using artificial neural networks and fuzzy logic as one of the most advanced and perspective directions of solving this task. A theoretical approach to the issues discussed is combined with the data of experimental studies implemented with application of load curves of regional electrical power systems. The book is addressed to specialists and researchers concerned with operational control modes of electric power systems.
Biografía del autor
Galina P. Shumilova, Ph.D. (Technical Sciences), St.PSTU, a senior researcher at the ISE&EPN Komi SC UB RAS, Syktyvkar, the Russian Federation (RF).Natalia E. Gotman, SSU, a research fellow of the ISE&EPN Komi SC UB RAS, Syktyvkar, the RF.Tatiana B. Starceva, Komi SPI, a research fellow of the ISE&EPN Komi SC UB RAS, Syktyvkar, the RF.
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Electric load forecasting using an artificial neural networks
Verlag:
LAP LAMBERT Academic Publishing Mär 2014, 2014
ISBN 10: 3659459380
ISBN 13: 9783659459382
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Taschenbuch. Zustand: Neu. Neuware -Electric load forecasting is an important research field in electric power industry. It plays a crucial role in solving a wide range of tasks of short-term planning and operating control of electric power system operating modes. Load forecasting is carried out in different time spans. Load forecasting within a current day ¿ operating forecasting; one-day-week-month-ahead load forecasting ¿ short-term load forecasting; one-month-quarter-year-ahead load forecasting ¿ long-term load forecasting. So far a great number of both conventional and non-conventional electric load forecasting methods and models have been developed. The work presents research results of electric load forecasting for electrical power systems using artificial neural networks and fuzzy logic as one of the most advanced and perspective directions of solving this task. A theoretical approach to the issues discussed is combined with the data of experimental studies implemented with application of load curves of regional electrical power systems. The book is addressed to specialists and researchers concerned with operational control modes of electric power systems.Books on Demand GmbH, Überseering 33, 22297 Hamburg 100 pp. Englisch. Artikel-Nr. 9783659459382
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