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A Novel Neural Network-Based Multiobjective Evolution Lower Upper Bound Estimation Method for Electricity Load Interval Forecast
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DOI：10.1109/TSMC.2024.3352665.png)
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摘要
En 中文
Currently, an interval prediction model, lower and upper bounds estimation (LUBE) which constructs the prediction intervals (PIs) by using the double outputs of the neural network (NN) is growing popular. However, existing LUBE researches have two problems. One is that the applied NNs are flawed: feedforward NN (FNN) cannot map the dynamic relationship of data and recurrent NN (RNN) is computationally expensive. The other is that most LUBE models are built under a single-objective frame in which the uncertainty cannot be fully quantified. In this article, a novel wavelet NN (WNN) with direct input-output links (DLWNN) is proposed to obtain PIs in a multiobjective LUBE frame. Different from WNN, the proposed DLWNN adds the direct links from the input layer to output layer which can make full use of the information of time series data. Besides, a niched differential evolution nondominated fast sort genetic algorithm (NDENSGA) is proposed to optimize the prediction model, so as to achieve a balance between estimation accuracy and the average width of the PIs. NDENSGA modifies the traditional population renewal mechanism to increase population diversity and adopts a new elite selection strategy for obtaining more extensive and uniform solutions. The effectiveness of DLWNN and NDENSGA is evaluated through a series of experiments with real electricity load data sets. The results show that the proposed model has better performance than others in terms of convergence and diversity of obtained nondominated solutions.
Keyword:
Forecasting
Artificial neural networks
Time series analysis
Predictive models
Load modeling
Estimation
Upper bound
Multiobjective optimization
short-term load interval prediction
uncertainty forecasting
wavelet neural networks (WNNs)
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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引用论文
A Multiple Model Approach to Time-Series Prediction Using an Online Sequential Learning Algorithm使用在线顺序学习算法进行时间序列预测的多模型方法
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Proceedings. Tenth International Conference on Parallel and Distributed Systems, 2004. ICPADS 2004.
Proceedings. Tenth International Conference on Parallel and Distributed Systems, 2004. ICPADS 2004.IF0
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