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雷达卡
摘要翻译：
短期电价预测已成为需求侧管理和发电调度的重要内容。特别是随着电力市场竞争的加剧，一个比独立系统运营商(ISO)发布的日前边际电价(DALMP)更准确的电价预测将有利于市场参与者增加利润或改善负荷需求调度。因此，本文的主要思想是利用已发表的DALMP、历史实时LMP(RTLMP)和其他有用信息，利用自回归积分滑动平均(ARIMA)模型来获得比DALMP更好的LMP预测。首先，建立了一套利用DALMP和历史RTLMP的季节性ARIMA(SARIMA)模型，并与利用DALMP和RTLMP在预测能力上的差异的自回归滑动平均(ARMA)模型进行了比较。通过引入价格波动性，建立了广义自回归条件异方差(GARCH)模型，进一步改进了预测方法。这些模型是用大陆中部独立系统运营商(MISO)地区的真实市场数据进行训练和评估的。评价结果表明，ARMAX-GARCH模型以外生时间序列表示周末日，提高了短期电价预测精度，优于其他ARIMA模型
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英文标题：
《Improving Short-Term Electricity Price Forecasting Using Day-Ahead LMP
with ARIMA Models》
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作者：
Zhongyang Zhao, Caisheng Wang, Matthew Nokleby, Carol Miller
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
Short-term electricity price forecasting has become important for demand side management and power generation scheduling. Especially as the electricity market becomes more competitive, a more accurate price prediction than the day-ahead locational marginal price (DALMP) published by the independent system operator (ISO) will benefit participants in the market by increasing profit or improving load demand scheduling. Hence, the main idea of this paper is to use autoregressive integrated moving average (ARIMA) models to obtain a better LMP prediction than the DALMP by utilizing the published DALMP, historical real-time LMP (RTLMP) and other useful information. First, a set of seasonal ARIMA (SARIMA) models utilizing the DALMP and historical RTLMP are developed and compared with autoregressive moving average (ARMA) models that use the differences between DALMP and RTLMP on their forecasting capability. A generalized autoregressive conditional heteroskedasticity (GARCH) model is implemented to further improve the forecasting by accounting for the price volatility. The models are trained and evaluated using real market data in the Midcontinent Independent System Operator (MISO) region. The evaluation results indicate that the ARMAX-GARCH model, where an exogenous time series indicates weekend days, improves the short-term electricity price prediction accuracy and outperforms the other proposed ARIMA models
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PDF链接：
https://arxiv.org/pdf/1801.02485
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