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Study of artificial neural network based short term load forecasting

机译:基于人工神经网络的短期负荷预测研究

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With more and more renewable energy integrated into the power grid and demand response in smart grid environment, electric load forecasting becomes more important. Accurate load forecasting facilitates better renewable energy integration and electricity market operation. Over the years, different load forecasting methods have been developed and applied. Multiple linear regression and artificial neural network based methods are well accepted by industries. This paper focuses on ANN-based method and provides detailed steps of load forecasting including data processing and neural network design.
机译:随着越来越多的可再生能源集成到电网中以及智能电网环境中的需求响应,电力负荷预测变得越来越重要。准确的负荷预测有助于更好的可再生能源整合和电力市场运营。多年来,已经开发并应用了不同的负荷预测方法。多元线性回归和基于人工神经网络的方法已为业界所接受。本文着重于基于ANN的方法,并提供了负荷预测的详细步骤,包括数据处理和神经网络设计。

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