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Short term load forecasting of Indian system using linear regression and artificial neural network

机译:基于线性回归和人工神经网络的印度系统短期负荷预测

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The hour ahead load forecasting is used for the reliable and proactive operation of the power system. The hour ahead load forecasting is a one type of Short Term Load Forecasting (STLF). The mostly STLF is used for the spinning reserve capacity, unit commitment and maintenance planning in the power system. In this paper the Linear Regression (LR) and the Artificial Neural Network (ANN) are used to study the STLF. In the ANN feed forward network is used for the hourly load forecasting. One fast training algorithm the Levenberg-Marquardt Back Propagation (LMBP) is used to train the neural network. The neuron model is trained using the historical load data of Indian distribution system. The sensitivity of the weather data for the STLF is verified. Both the techniques the LR and the ANN are compared according to the Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE). The accuracy of the ANN technique for the STLF with the weather data is proved for the residential and the industrial feeder.
机译:提前一个小时的负载预测可用于电力系统的可靠和主动运行。提前一个小时的负荷预测是短期负荷预测(STLF)的一种类型。 STLF主要用于电力系统中的旋转备用容量,机组承诺和维护计划。在本文中,线性回归(LR)和人工神经网络(ANN)用于研究STLF。在ANN中,前馈网络用于每小时负荷预测。 Levenberg-Marquardt反向传播(LMBP)是一种快速训练算法,用于训练神经网络。使用印度配电系统的历史负荷数据训练神经元模型。验证了STLF的天气数据的敏感性。根据平均绝对误差(MAE)和平均绝对百分比误差(MAPE)比较LR和ANN的两种技术。证明了STLF的ANN技术与天气数据的准确性,适用于住宅和工业支线。

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