本研究運用TGOS圖資，並結合機器學習、網路爬蟲、開放資料與RWD等技術建置一套整合能源資訊與評估的GoGoEnergy系統。本研究所開發之系統利用視覺化技術將相關政府開放資料庫數據資料呈現，另外，透過這些數據資料進行統計資料分析，例如風力發電廠建置評估，運用所蒐集之各地區平均風速和風能作為評估一般型風力發電機組建置後帶來的效益。同時，將歷年颱風入侵資料透過機器學習進行分析，進而獲得台灣各地區颱風入侵機率，並以其平均風速作為評估指標，作為可開發建置「颱風型風力發電機組」依據。此外，本研究亦提供了太陽能發電站的建置評估功能，讓有需求的使用者能先行分析建置太陽能板發電所帶來的效益與適合度。同時以風向風速套疊不同氣象因子與不同層場的預報資料，在地圖上以色彩豐富的動畫方式呈現未來天氣系統的變化趨勢，將數值預報資料以易解讀與視覺化的圖像訊息，讓使用者能輕鬆理解天氣變化相關資訊。

In this study, we used TGOS' graphic, machine learning, open data, web crawler, and RWD technologies, combined with government open data to develop an energy information system named GoGoEnergy. The system uses visualization technology to present government open-data and analyze it. For example, in the function “wind power plant construction assessment”, it could evaluate the benefits of a wind-power plant after installation by the average wind-speed and wind-energy. Moreover, the historic typhoon instruction data are analyzed through machine learning to obtain the probability of typhoon intrusion in Taiwan. The average wind-speed of the typhoon could be used as an evaluation of “typhoon-type wind power plant”. Beside the wind-power, the system also provides the evaluation for solar power. Users could realize the benefits and suitability of solar panels. This system could overlay forecast data of different meteorological factors and different horizons with wind direction, speed and present the changing of the future weather with colorful animation on the map. Therefore, the system would make the numerical data converted into easy-to-interpret and visualized image information, allowing users to easily understand information of weather changes.