基於極限學習機及多解析度分析之短期負載預測

Short-term Load Forecasting Based on Extreme Learning Machines and Multi-resolution Analysis

張富傑(Fu-Chieh Chang)

指導教授 ： 陳文輝 ； 吳漢雄

國立臺北科技大學/機電學院/自動化科技研究所/碩士(2010年)

https://doi.org/10.6841/NTUT.2010.00120

摘要

本文提出應用極限學習機與多解析度分析於短期負載預測問題，並討論三種演算法應用在短期負載預測存在何種問題及限制。討論的方法有差分自迴歸移動平均模型、倒傳遞類神經網路及灰色滾動模型。負載資料取自台灣北部某二次變電所，該變電所配置四台額定容量為250MVA之變壓器。預測時間為2008年夏季每日每小時該變電所消耗的負載量。計算誤差的公式為均方根誤差及絕對平均誤差百分比。實驗結果顯示結合多解析度分析與極限學習機模型的預測方法不僅誤差較其它三種方法低，並能有效解決其它三種方法中使用上的限制。

關鍵字

極限學習機 ； 多解析度分析 ； 短期負載預測

並列摘要

An effective approach for short-term load forecasting by using hybrid extreme learning machines and multi-resolution analysis was proposed in this thesis. Two studied cases were provided to verify the effectiveness of the proposed approach. To further examine the performance, a comparison with existing methods including autoregressive integrated-moving average models, back propagation neural networks, and grey rolling models was given to highlight the merit of the proposed approach. The hourly load data for this experiment are derived from a realistic Taipower substation with four 250 MVA transformers during the summer period of 2008. The performance is evaluated by RMSE (root mean square error) and MAPE (mean absolute percentage error). Experimental results showed that the proposed approach is superior to existing methods.

並列關鍵字

Extreme learning machines ； Multi-resolution analysis ； Short-term load forecasting

參考文獻

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