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首页> 外文会议>2015 International Conference on Energy Economics and Environment >Short-term load/price forecasting in deregulated electric environment using ELMAN neural network
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Short-term load/price forecasting in deregulated electric environment using ELMAN neural network

机译：使用ELMAN神经网络的管制电力环境下的短期负荷/价格预测

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Load forecasting plays a significant role in power system planning. In today's scenario of deregulated electricity market as existing in New South Wales (NSW) Australia, an extremely accurate load/ price forecasting model is required because of several economic and operational advantages. It helps in dealing with the problems of economic load dispatch, unit commitment, protection, etc. Research shows that most of the classical methods are incapable to forecast the load/ price with highest possible precision, as per the expectation of deregulated and complex electricity markets. In this paper, Artificial Neural Network (ANN)-based Short Term Load Forecasting (STLF) model, i.e., ELMAN Neural Network (ELMNN) is developed and tested on NSW Australia data. The performance of the ELMNN-based model is compared with Feed Forward Neural Network (FFNN) and Radial Basis Function Neural Network (RBFNN). It is observed that ELMNN-based load forecasting model produces superior results over other ANN-based models.
机译：负荷预测在电力系统规划中起着重要作用。在当今澳大利亚新南威尔士州（NSW）放宽电力市场管制的情况下，由于具有多种经济和运营优势，因此需要非常精确的负荷/价格预测模型。它有助于解决经济负荷分配，机组承诺，保护等问题。研究表明，按照放松管制和复杂的电力市场的预期，大多数经典方法都无法以尽可能高的精度预测负荷/价格。 。在本文中，开发了基于人工神经网络（ANN）的短期负荷预测（STLF）模型，即ELMAN神经网络（ELMNN）并在新南威尔士州澳大利亚数据上进行了测试。将基于ELMNN的模型的性能与前馈神经网络（FFNN）和径向基函数神经网络（RBFNN）进行了比较。可以观察到，基于ELMNN的负荷预测模型产生的结果优于其他基于ANN的模型。

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