论文标题
短期电价预测的混合通用长期存储模型的预测精度
Predictive Accuracy of a Hybrid Generalized Long Memory Model for Short Term Electricity Price Forecasting
论文作者
论文摘要
准确的电价预测是市场参与者的主要管理目标，因为它代表了最大化市场参与者利润的基本基础。但是，电力是一种不可动摇的商品，电价受到一些社会和自然因素的影响，这些因素和自然因素使价格预测到了一项艰巨的任务。这项研究研究了基于广义的长期记忆自回旋模型（K-Factor Garma）的新混合模型的预测性能，Gegenbauer广义自动回收自动回归有条件的异方差（G-GARCH）过程，小波型分解和使用两种不同的学习Algorith的优化;反向传播算法（BP）和粒子群优化算法（PSO）。使用NORD池电力市场的数据评估了拟议模型的性能。此外，它与其他一些参数和非参数模型进行了比较，以证明其稳健性。经验结果证明，所提出的方法比其他竞争技术表现良好。
Accurate electricity price forecasting is the main management goal for market participants since it represents the fundamental basis to maximize the profits for market players. However, electricity is a non-storable commodity and the electricity prices are affected by some social and natural factors that make the price forecasting a challenging task. This study investigates the predictive performance of a new hybrid model based on the Generalized long memory autoregressive model (k-factor GARMA), the Gegenbauer Generalized Autoregressive Conditional Heteroscedasticity(G-GARCH) process, Wavelet decomposition, and Local Linear Wavelet Neural Network (LLWNN) optimized using two different learning algorithms; the Backpropagation algorithm (BP) and the Particle Swarm optimization algorithm (PSO). The performance of the proposed model is evaluated using data from Nord Pool Electricity markets. Moreover, it is compared with some other parametric and non-parametric models in order to prove its robustness. The empirical results prove that the proposed method performs well than other competing techniques.
