近期對大多數產業而言，獲得電力是一個非常重要的議題，如製造業、服務業、政府或農業等。由於使用不可再生資源發電，全球碳排放正在增加，然而，雖不可再生能源是一個解決方案，但因其可變性和不確定性，這種能源的發展並不顯著，這足以顯示出需求預測的重要性，供給應該更加精確，以避免過度使用能源。有幾種方法可以用於電力預測，長短期記憶模型（long-short term memory; LSTM）是一種有機會的演算法。 LSTM被延伸開發為嵌套LSTM（nested long-short term memory; NLSTM），它擁有LSTM所不具備的優勢。此外，注意力機制（attention mechanism; AM）已被證明可以提高深度學習的性能，因此本研究將NLSTM與AM結合以進行預測。由於有一些參數可能會影響網絡的性能，本研究採用萬用演算法來尋找合適的參數，整合基因演算法（genetic algorithm; GA）和布穀鳥搜索優化器（cuckoo search optimizer; CSO），稱為基因CSO，並應用它來確定NLSTM-AM參數，以達到更好的網絡結構。為了評估所提出的算法的性能，本研究使用兩個數據集在均方根誤差（root mean squared error; RMSE）與一些現有演算法進行了比較。計算結果顯示，本研究所提出的演算法可以有較低的RMSE。此外，GA和CSO的整合確實能夠超過GA或CSO的單獨表現。

Recently, acquiring electricity has become a very important issue for most of sectors such as manufacturing, service, government, or agricultural. Due to electricity generation by non-renewable source, global emission is getting to increase. Non-renewable energy source is the solution, but the development of this energy is not significance especially due to its variability and uncertainty. This issue is enough to force the importance of demand forecasting and the supply should be more precise and to avoid the excessive usage of energy source. There are several methods for electricity forecasting and long-short term memory (LSTM) is one of promising algorithms. One of LSTM variants was developed as nested LSTM (NLSTM) which owns the advantages that LSTM does not have. In addition, attention mechanism (AM) which has been proved can enhance deep learning performance. Thus, the current study intends to combine NLSTM with AM for forecasting. However, there are some parameters which may influence the network performance. Therefore, metaheuristic is employed to find the suitable parameters. Thus, this study integrates genetic algorithm (GA) and cuckoo search optimizer (CSO) called as genetic CSO and applies it to determine the NLSTM-AM parameters in order to reach better network structure. In order to assess the proposed algorithm’s performance, it is compared with some existing algorithms using two datasets in terms of root mean squared error (RMSE). The computational results show that the proposed algorithm can have lower RMSE. Besides, integration of GA and CSO really can outperform GA or CSO individually.

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