Modeling and forecasting of electricity spot-prices: Computational intelligence vs classical econometrics

Silvano Cincotti; Giulia Gallo; Linda Ponta; Marco Raberto

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Modeling and forecasting of electricity spot-prices: Computational intelligence vs classical econometrics

机译：电力现货价格的建模和预测：计算智能与经典计量经济学

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摘要

In European countries, the last decade has been characterized by a deregulation of power production and electricity became a commodity exchanged in proper markets. This resulted in an increasing interest of the scientific community on electricity exchanges for modeling both market activity and price process. This paper analyzes electricity spot-prices of the Italian Power Exchange (IPEX) and proposes three different methods to model prices time series: a discrete-time univariate econometric model (ARMA-GARCH) and two computational-intelligence techniques (Neural Network and Support Vector Machine). Price series exhibit a strong daily seasonality, addressed by analyzing separately a series for each of the 24 hours. One-day ahead forecasts of hourly prices have been considered so to compare the prediction performances of three different methods, with respect to the canonical benchmark model based on the random walk hypothesis. Results point out that Support Vector Machine methodology gives better forecasting accuracy for price time series, closely followed by the econometric technique.

机译：在欧洲国家，过去十年的特点是放宽了对电力生产的管制，电力成为了在适当市场上交换的商品。这引起了科学界对电力交换以建立市场活动和价格过程模型的兴趣日益增加。本文分析了意大利电力交易所（IPEX）的现货电价，并提出了三种不同的价格时间序列模型：离散时间单变量计量经济模型（ARMA-GARCH）和两种计算智能技术（神经网络和支持向量）机）。价格系列具有很强的每日季节性，可以通过分别分析24小时中的每个系列来解决。考虑了每小时价格的提前一天预测，因此可以比较三种不同方法的预测性能，以及基于随机游走假设的规范基准模型。结果指出，支持向量机方法可为价格时间序列提供更好的预测准确性，紧随其后的是计量经济学技术。

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来源
《AI communications》 |2014年第3期|301-314|共14页
作者
Silvano Cincotti; Giulia Gallo; Linda Ponta; Marco Raberto;
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作者单位

DIME-CINEF, University of Genoa, Genoa, 16145, Italy;

DIME-CINEF, University of Genoa, Genoa, Italy;

DIME-CINEF, University of Genoa, Genoa, Italy;

DIME-CINEF, University of Genoa, Genoa, Italy;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Electricity spot-prices; Neural Networks; SVM; ARMA-GARCH;

机译：电力现货价格;神经网络;支持向量机;阿玛·格奇;

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2. Forecasting electricity spot-prices using linear univariate time-series models [J] . Jesus Crespo Cuaresma, Jaroslava Hlouskova, Stephan Kossmeier, Applied Energy . 2004,第1期

机译：使用线性单变量时间序列模型预测电力现货价格
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4. Hybridisation of Fundamental and Composite Econometric Modelling for Short-Term Electricity Price Forecasting [C] . Rodrigo A. de Marcos, Antonio Bello, Javier Reneses International Conference on the European Energy Market . 2018

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5. Evaluating Forecasting Performance in the Context of Process-Level Decisions: Methods, Computation Platform, and Studies in Residential Electricity Demand Estimation. [D] . Huntsinger, Richard A. 2017

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7. Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market [O] . George P. Papaioannou, Christos Dikaiakos, Anargyros Dramountanis, 2016

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8. Forecasting Electricity Demand with End-Use/Econometric Models [R] . King, M. J., Scott, M. J. 1983

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Modeling and forecasting of electricity spot-prices: Computational intelligence vs classical econometrics

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