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題名：基因演算法結合長短期記憶於短期電力負荷預測
作者：莎曼塔
作者(外文)：ARPITA SAMANTA SANTRA
校院名稱：元智大學
系所名稱：資訊管理學系
指導教授：林志麟 博士
學位類別：博士
出版日期：2019
主題關鍵詞：長短期記憶模型；基因演算法；短期負載預測；電力負載預測；多元時間序列；Long Short Term Memory (LSTM)；Genetic algorithm (GA)；Short Term Load Forecasting (STLF)；Electricity Load Forecasting；multivariate time series
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對於電力公用事業能準確的預測負荷是一項很重要的任務，可提高電力系統的效能和相關運行，且能在制定電價的政策上給予參考，如果能預測未來24小時的電力附載，營運商就能參考預測結果做出規劃與優化資源的利用。本研究提出的短期負荷預測（STLF）方法尚未被其他研究提出，目的是利用長期短期記憶（LSTM）結合基因演算法（GA）更精確的預測短期電力負荷。本研究方法使用實際電力負荷與天氣條件作為訓練資料與建模，並進行電力負荷預測。長期短期記憶（LSTM）網路具有長期依賴性的缺點，本研究使用基因演算法（GA）計算長期短期記憶（LSTM）模組的最佳參數，解決長期依賴性的問題發生。長期短期記憶（LSTM）搭配最佳參數適用於局部最優搜索，因此本研究結合長期短期記憶（LSTM）、基因演算法（GA）的優點，更能夠有效預測次日的每小時電力負荷。
Electricity load forecasting is an important task to enhance energy efficiency and operation reliability of the power system. Forecasting the hourly electricity load of the next day assists optimizing the resources and minimizing the energy wastage. The main motivation of this study is to improve the robustness of short-term load forecasting (STLF) by utilizing long short term memory (LSTM) and genetic algorithm (GA). The proposed method is novel: LSTM networks are designed to avoid the problem of long-term dependencies, and GA is used to obtain the optimal LSTM’s parameters, which are then applied to predict the hourly electricity load for the next day. The proposed method is trained using actual load and weather data, and the performance results show that it yields small mean absolute percentage error on the test data.
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