植物微生物燃料電池(Plant Microbial Fuel Cell, PMFC)係一種新興的綠色能源技術，能持續將太陽能轉化成電能，於建築物屋頂上放置植物微生物燃料電池(PMFC)，不僅綠化都市環境，亦產生電力供給城市使用。PMFC產電量性能受多樣環境因素影響，難以精確預估產電量，因此本研究比較人工智慧中的淺層及深層學習技術，應用仿生優化演算法建立PMFC產電量人工智慧預測模型，推估PMFC裝置未來可達的蓄電量。所建置預測模型，係以感測器蒐集2018年3月至6月狼尾草、香蒲、圓葉節節菜的產電數據及其對應的裝置參數與環境因子，在資料預處理過程中剔除不適合草種圓葉節節菜，以狼尾草、香蒲PMFC的裝置、周圍環境參數及產電量共39個因子做為原始訓練資料。本研究原始資料為數值形式，用於淺層學習及時序性深度學習的模型訓練；另以滑動視窗原理建立數值矩陣，進而將數值矩陣轉換成2D圖像格式(image-like data)，做為在電腦視覺領域具前瞻發展性的深度卷積神經網路模型之圖像識別資料。分析成果顯示，深度學習卷積神經網路中的EfficientNet為最適配模型，為提升EfficientNet的泛化能力，進而整合生物啟發式優化演算法-水母演算法(Jellyfish Search, JS)決定最佳超參數，建立混合模型JS-EfficientNet。研究成果及效益如下：(1)產電量人工智慧預測模型敏感度分析顯示植物品種、電池裝置參數及環境因子確為影響PMFC產電量。未來研究人員能藉開發之預測模型，控制相關因子變數，避免重複性及不必要的實驗配置，簡化流程及減少成本；(2)能源管理單位及節能單位藉PMFC產電量預測模型，可預先規劃區域PMFC產電量、輔助電力高峰時段；(3)PMFC產電量的預測可應用於自我維持無線感測網路中的DPM與超級電容設計，提供WSN系統預判切換模式，減少資料傳輸錯誤及延長系統壽命。

Plant Microbial Fuel Cell (PMFC) is an emerging green energy technology that can continuously convert solar energy into electricity. Placing a plant microbial fuel cell (PMFC) on the roof of a building can green the urban environment and generate electricity for use in the city. PMFC electricity production performance is affected by various environmental factors, and is not easy to estimate accurately. Therefore, this study compares shallow and deep learning methods in artificial intelligence; utilizes a metaheuristic optimization algorithm to build a PMFC electricity production artificial intelligence prediction model, and estimates the amount of storage capacity that PMFC devices might reach in the future. The prediction model uses sensors to collect electricity production data, device parameters and environmental factors of Pennisetum alopecuroides, Narrowleaf cattail and Rotala rotundifolia from March to June 2018. In the process of data preprocessing, the unsuitable Rotala rotundifolia is eliminated, and 39 factors, including Pennisetum alopecuroides and Narrowleaf cattail PMFC devices, surrounding environmental parameters and electricity production, are used as the original training data. The original numerical data are used for model training of shallow learning and time-series deep learning. Additionally, a numerical matrix is established based on the sliding window principle, and then converted into a 2D image format (image-like data) as Image recognition data of a forward-looking deep convolutional neural network model in the field of computer vision. Analytical results indicate that EfficientNet using a deep learning convolutional neural network is the most suitable model. To improve the generalization ability of EfficientNet, a metaheuristic optimization algorithm, Jellyfish Search (JS), is added to determine the best hyperparameters, forming a hybrid model JS-EfficientNet. The research results and benefits are as follows. (1) Sensitivity analysis of the Artificial Intelligence prediction model for electricity production demonstrates that plant species, device parameters and environmental factors affect PMFC electricity production. Future research could use the developed predictive model to control the relevant factors and variables, avoid repetitiveness and unnecessary experimental configuration, simplify the process and reduce costs. (2) The energy management unit and the Energy-saving unit can pre-plan the regional PMFC by using the PMFC power generation prediction model Power generation and auxiliary power peak hours. (3) PMFC power generation prediction can be applied to the design of the DPM and supercapacitors in self-sustaining wireless sensing networks, providing a predictive switching mode in the WSN system, lowering data transmission errors and extending system life.

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