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Research on short-term electric load forecasting based on extreme learning machine

机译:基于极限学习机的短期电力负荷预测研究

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As an important support for the development of the national economy, the power industry plays a role in ensuring economic operations. Time series prediction can process dynamic data, is widely used in economics and engineering, and especially is of great practical value in using historical data to predict future development. Under the guidance of extreme learning machine and time series theory, this paper applies the extreme learning machine to the study of time series, and builds a model for load forecasting research. Load forecasting plays an important role in power planning, affecting planning operation modes, power exchange schemes, etc., so load forecasting is very necessary in power planning. First, establish an extreme learning machine model; second, the short-term load forecasting is performed by different activation functions to verify the performance of the activation function.~(1)After empirical analysis, the activation function with the best predictive ability is obtained.
机译:电力工业作为国民经济发展的重要支撑,在确保经济运行中发挥着重要作用。时间序列预测可以处理动态数据,在经济学和工程学中得到广泛应用,特别是在使用历史数据预测未来发展方面具有很大的实用价值。在极限学习机和时间序列理论的指导下,将极限学习机应用于时间序列的研究,建立了负荷预测研究模型。负荷预测在电力计划中起着重要作用,影响计划的运行模式,电力交换方案等,因此负荷预测在电力计划中非常必要。首先,建立极限学习机模型;其次,通过不同的激活函数进行短期负荷预测,以验证该激活函数的性能。〜(1)通过实证分析,获得具有最佳预测能力的激活函数。

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