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考量影響碳足跡之屋頂太陽能政策 系統動態模型研究

為了解決Bp stock London的問題，作者Thi Hang Dinh 這樣論述：

Solar photovoltaic (PV) system has been one of the most important solutions to reduce the dependence on imported energy supply and impacts on the environment in Taiwan. Recently, the building sector could be seen as a major driver for next years during climate change and global warming contexts. Ho

wever, the world community has made only little and insufficient progress in the building sector in terms of energy savings and greenhouse gas reductions. Therefore, this research addresses the impacts of rooftop solar PV installations on residential and commercial buildings to identify the potentia

l of GHG emissions reduction in Taiwan. The purpose is to analyze the influence of rooftop solar PV installation on greenhouse gases under the government policy focusing especially on Feed-in Tariff (FIT) and government subsidy for PV installation costs. Accordingly, a total of 11 policy strategies

(five single and six hybrid policies) is proposed and how they influence the number of rooftop solar PV installations on buildings and corresponding carbon footprint from 2021 to 2050. The results show that the hybrid policy with FIT 1% increment and government subsidy of 50% can help achieve the lo

west carbon footprint compared to the other policies. Besides suggesting the best policy to the government, this research also hopes to help raise the awareness of people on the benefit of using solar panels from both economic and environmental perspectives.

機器學習結合波動聚集分析之投資策略實證研究

為了解決Bp stock London的問題，作者李強 這樣論述：

量化投資和機器學習在大數據時代充分展現了其獨特的優勢和魅力，兩者結合更是如虎添翼。機器學習不僅可以彌補量化投資的短板，還可以為量化投資的發展提供新的思路和方向。本文主要研究是否可以通過機器學習算法進行因子合成，以及該方法是否比傳統方法更有效。選取波動率、年化收益率、最大回撤、信息比率、夏普比率等評價指標進行因子分層回測結果的對比分析。本文認為機器學習算法對量化投資和股票預測具有一定的重要性影響，為投資者的決策提供可行的解決方案。另外，本文詳細介紹了波動聚集現象和金融時間序列模型。然後本文將波動聚集性作為因子加入到策略的機器學習部分進行應用，以原策略為對照，並對回測結果進行詳細對比分析。綜上可

見，波動聚集現象的實際應用是值得研究的，這樣可以提高量化策略的性能和穩健性，同時對波動聚集現象的應用和發展提供了新想法。本文對 XGBoost 算法和量化投資中的波動聚集現象進行了研究和改進，為滬深 300 股票市場的量化投資者提供了一個新觀點。