兰州大学机构库 >数学与统计学院
基于懒惰学习的优化算法及组合预测模型的研究与应用
Alternative TitleThe research and application of optimization model based on the lazy learning algorithm and neuro network combination models
宋一辽
Thesis Advisor王建州
2015-05-24
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name硕士
Keyword特征提取 组合模型 多步预测 EEMD去噪 懒惰学习
Abstract人工神经网络自从其提出后已被成功运用到信息处理、模式识别、智能控制及系统建模等多个领域。在预测方面,国内外学者通过对多种神经网络算法进行组合或者混合得到了良好的预测效果。本文通过对电力负荷和风速数据进行模拟实验,研究神经网络组合模型在实际预测中的效果和稳定性。研究表明在竞争激烈的电力市场,由于自身的不稳定性以及各种其他因素的干扰,电力负荷序列特征难以被精确提炼。因此,如何提高电力负荷预测精度已成为电力市场的发展和完善的关键环节。为了有效解决这一难题,本文提出了基于支持向量机(SVM)、自适应神经模糊推理系统(ANFIS)、极限学习机(ELM)、特征提取和优化算法(OA)的时间序列组合优化模型(TSCOM)预测未来一天的电力负荷。这种新模式成功地吸取了每个基本模型的优点,同时还考虑了时间序列的特征。相比简单地给每个模型一个特定的权重将其组合起来,这种特征提取并结合优化算法的方法可以获得更好的预测精度。为了验证这个新模型——TSCOM的预测精度,本文分析、模拟了澳大利亚昆士兰州的电力负荷数据,仿真结果表明该模型在预测精度上确实优于单一的SVM、ANFIS和ELM模型。由于多步风速预测方法的限制和不确定性,本文基于去噪、曲线调整、懒惰学习算法、多输出预测策略和验证的布谷鸟算法提出了一种新的混合预测模型ALL-DDVC,该模型能在有异常数据的情况下进行多步风速预测。此外,四个风电场十分钟风速数据的验证表明ALL-DDVC模型对于长时间的风速的多步预测是实用、有效的。
Other AbstractNeural network has been successfully applied in many fields such as information processing,pattern recognition,intelligent control and system modeling since it was first proposed.Moreover,researches have used combination or hybrid model based on neural network algorithms for forecasting and got good results.This paper analyses effectiveness and robustness of neural network combination models in practical prediction by simulating electricity load and wind speed data.In a competitive electricity market,electricity load series has proved to be highly complex due to its nonstationarity as well as various unstable factors.Thus,how to address forecasting accuracy is an important challenge in an era in which electricity market is increasing significant.Based on Support Vector Machine (SVM),Adaptive Neuro-Fuzzy Inference System (ANFIS),Extreme Learning Machine (ELM),method of feature extraction and Optimization Algorithm (OA),this paper proposes Time Series Combination Optimization Model (TSCOM) for day ahead electricity load forecasting.This novel model successfully combines advantages of each basic model meanwhile considering characteristics of time series rather than simply giving each model a specific weight.This method of feature extraction makes OA can obtain a relatively decent weights to combine basic models and then improve forecasting accuracy.For the purpose of analyzing and validating forecasting effectiveness of TSCOM,the electricity load in Queensland,Australia is selected as database in this paper.Finally,the simulation result shows the proposed model indeed outperforms SVM,ANFIS and ELM. Due to the limitations and uncertainties of the multi-step wind speed forecasting method, this paper proposes a novel hybrid forecasting model,the application of the adjusted Lazy Learning model based on a denoising,decomposed multi-output strategy and improving the Validation Cuckoo search (ALL-DDVC).The proposed model is not only capable of multi-step ahead wind speed forecasting with abnormal data,but is also effective and robust according to valid experimental simulations of ten-min-interval wind speed data from four wind farms.Therefore,the ALL-DDVC method is practical and effective for multi-step and long-horizon wind speed forecasting.
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/225144
Collection数学与统计学院
Recommended Citation
GB/T 7714
宋一辽. 基于懒惰学习的优化算法及组合预测模型的研究与应用[D]. 兰州. 兰州大学,2015.
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