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首页> 外文期刊>Technical Gazette >Week Ahead Electricity Price Forecasting Using Artificial Bee Colony Optimized Extreme Learning Machine with Wavelet Decomposition
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Week Ahead Electricity Price Forecasting Using Artificial Bee Colony Optimized Extreme Learning Machine with Wavelet Decomposition

机译：一周前方电价预测采用人造蜂殖民地优化极限学习机，小波分解

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Electricity price forecasting is one of the more complex processes, due to its non-linearity and highly varying nature. However, in today's deregulated market and smart grid environment, the forecasted price is one of the important data sources used by producers in the bidding process. It also helps the consumer know the hourly price in order to manage the monthly electricity price. In this paper, a novel electricity price forecasting method is presented, based on the Artificial Bee Colony optimized Extreme Learning Machine (ABC-ELM) with wavelet decomposition technique. This has been attempted with two different input data formats. Each data format is decomposed using wavelet decomposition, Daubechies Db4 at level 6; all the decomposed data are forecasted using the proposed method and aggregate is formed for the final prediction. This prediction has been attempted in three different electricity markets, in Finland, Switzerland and India. The forecasted values of the three different countries, using the proposed method are compared with various other methods, using graph plots and error metrics and the proposed method is found to provide better accuracy.
机译：电价预测是由于其非线性和高度不同的流程，是更复杂的过程之一。然而，在当今的解调市场和智能电网环境中，预测价格是招标过程中生产者使用的重要数据源之一。它还有助于消费者知道每小时的价格，以管理每月电价。本文基于具有小波分解技术的人工蜂殖民地优化的极限学习机（ABC-ELM），提出了一种新型电价预测方法。这已经尝试了两种不同的输入数据格式。每个数据格式都使用小波分解进行分解，Daubechies DB4在6级;使用所提出的方法和聚集在最终预测中预测所有分解数据。在芬兰，瑞士和印度的三种不同电力市场中已经尝试了这一预测。使用所提出的方法的三个不同国家的预测值与各种其他方法进行比较，使用曲线图和错误指标，并发现所提出的方法提供更好的准确性。

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