Application of fuzzy neural networks and artificial intelligence for load forecasting
Application of fuzzy neural networks and artificial intelligence for load forecasting
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DOI：
10.1016/j.epsr.2003.12.012
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发表时间：
2004-08
影响因子：
3.9
通讯作者：
Gwo-Ching Liao;T. Tsao
中科院分区：
文献类型：
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作者：
Gwo-Ching Liao;T. Tsao
本文提出了一种集成进化模糊神经网络和模拟退火（AIFNN）的负荷预测方法。首先，我们使用模糊超矩形复合神经网络（FHRCNN）进行初始负载预测。然后，我们使用进化规划（EP）和模拟退火（SA）来找到FHRCNN参数（包括突触权重、偏差、隶属函数、隶属函数中的敏感因子和可调节突触权重等参数）的最优解。我们知道EP具有良好的搜索全局最优值的能力，但搜索局部最优值的能力较差。并且，SA仅具有良好的搜索局部最优值的能力。因此，我们将两种方法结合起来，以获得两种方法的优点，从而改善传统ANN训练中权重和偏差总是陷入局部最优的缺点。最后，我们使用 AIFNN 来看看是否可以提高解决方案的质量，以及是否确实可以减少负荷预测的误差。使用从样本研究中获得的数据（包括 1 年、1 个月和 24 小时时间段）对拟议的 AIFNN 负荷预测方案进行了测试。结果证明了所提出的负荷预测方案的准确性。
An integrated evolving fuzzy neural network and simulated annealing (AIFNN) for load forecasting method is presented in this paper. First we used fuzzy hyper-rectangular composite neural networks (FHRCNNs) for the initial load forecasting. Then we used evolutionary programming (EP) and simulated annealing (SA) to find the optimal solution of the parameters of FHRCNNs (including parameters such as synaptic weights, biases, membership functions, sensitivity factor in membership functions and adjustable synaptic weights). We knew that the EP has a good capability for searching for globe optimal value, but a poor capability for searching for the local optimal value. And, the SA only had a good capability for searching for a local optimal value. Therefore, we combined both methods to obtain both advantages, and so improve the shortcoming of the traditional ANN training where the weights and biases are always trapped into a local optimum. Finally, we use the AIFNN to see if we could improve the solution quality, and if we actually could reduce the error of load forecasting. The proposed AIFNN load forecasting scheme was tested using data obtained from a sample study including 1 year, 1 month and 24h time periods. The result demonstrated the accuracy of the proposed load forecasting scheme.