Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Dự đoán giá carbon bằng mô hình tích hợp phi tuyến đa quy mô kết hợp tái cấu trúc đặc trưng tối ưu và học sâu hai giai đoạn
Tóm tắt
Dự đoán chính xác giá carbon có ý nghĩa rất lớn đối với cả nhà quản lý và nhà đầu tư. Việc cải thiện dự đoán giá carbon không chỉ cung cấp cho nhà đầu tư những lời khuyên hợp lý mà còn thúc đẩy tiết kiệm năng lượng và giảm phát thải. Tuy nhiên, các phương pháp truyền thống không đạt hiệu quả cao trong dự đoán do tính phi tuyến và không tĩnh tại của giá carbon. Nghiên cứu này đề xuất một mô hình tích hợp phi tuyến đa quy mô sáng tạo nhằm cải thiện độ chính xác trong việc dự đoán giá carbon, kết hợp tái cấu trúc đặc trưng tối ưu và học sâu hai giai đoạn. Một mặt, tái cấu trúc đặc trưng tối ưu, bao gồm phân rã chế độ biến thiên (VMD) và độ entropi mẫu (SE), được sử dụng để hiệu quả khai thác các đặc trưng khác nhau từ giá carbon gốc. Mặt khác, học sâu hai giai đoạn dựa trên mạng nơ-ron hồi tiếp sâu (DRNN) và đơn vị cửa hồi tiếp (GRU) được áp dụng để dự đoán giá carbon. DRNN, một khung học sâu mới, được sử dụng để dự đoán từng thành phần. Đồng thời, GRU được sử dụng cho tích hợp phi tuyến, và dự đoán cuối cùng về giá carbon có thể được thu được thông qua quy trình này. Để minh họa và so sánh, nghiên cứu này sử dụng dữ liệu giá carbon từ Bắc Kinh, Hồ Bắc và Thượng Hải ở Trung Quốc như dữ liệu mẫu để kiểm tra khả năng của mô hình đề xuất. Kết quả thực nghiệm chứng minh rằng mô hình hybrid mới có thể cải thiện độ chính xác dự đoán giá carbon khi xem xét các phương pháp đo lường thống kê. Do đó, mô hình hybrid mới có thể được coi là một phương pháp hiệu quả trong việc dự đoán giá carbon.
Từ khóa
#dự đoán giá carbon #mô hình tích hợp phi tuyến #tái cấu trúc đặc trưng #học sâu hai giai đoạn #mạng nơ-ron hồi tiếp sâu #phân rã chế độ biến thiênTài liệu tham khảo
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