Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/291584
Title: Electrical forecasting optimization based on artificial intelligence techniques
Researcher: Singh Anamika
Guide(s): Manish Kumar Srivastava
Keywords: Engineering
Engineering and Technology
Engineering Electrical and Electronic
University: Sam Higginbottom Institute of Agriculture, Technology and Sciences
Completed Date: 2020
Abstract: newline Recently, utilization of nonlinear gadgets like power electronics, continuous power supplies, newlineflexible speed drives, and delicate loads like personal computers, etc has expanded. It is seen newlinethat nonlinearity in the electric load profile increases with the use of these devices. Therefore, newlinean accurate load forecasting is required to improve quality and quantity of power services. A newlinesignificant truth about the power is that it cannot be stored for quite a long time in AC form; newlineit is conceivable to store it in DC form, but it is restricted to a less amount comparing to newlinedemand and that too at an extreme high cost. Therefore, an accurate load forecasting is newlinerequired. Lower accuracy level can be accomplished by utilizing any conventional technique newlinehowever for higher accuracy; improved models are to be created. Therefore, the need for newlineaccurate and robust load forecasting model is evident in the current scenario of non linear newlineelectric load profile forecasting. newlineElectric forecasting (EF) is an important tool for power system operation, planning, and newlinecontrol for decisions such as load management, generation scheduling, and system security newlineassessment, etc. Most of the research is performed for short-term electric forecasting (STEF). newlineIt shows the importance of the STEF. In the literature, several robust and accurate newlineforecasting models were developed such as auto-regressive, auto-regressive integrated newlinemoving average and moving average and found capable of forecasting stationary time series newlinedata but real-time series is never stationary. These models were failed to provide the desired newlinelevel of accuracy with the nonlinearity present in electric load profile. Therefore, time-series newlinemodels are not suitable for accurate short-term load forecasting. STEF is related to newlineoperational tasks such as economic dispatch, fuel arrangement, load scheduling, etc. Thus, it newlinebecomes necessary to develop forecasting models with enhanced accuracy. newlineThe application of Artificial Intelligence (AI) techniques has been explored to
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URI: http://hdl.handle.net/10603/291584
Appears in Departments:Department of Electrical Engineering

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