Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Ước lượng bức xạ mặt trời bằng cách áp dụng đồng thời tái cấu trúc không gian pha và mô hình mạng nơ-ron lai
Tóm tắt
Việc ước lượng bức xạ mặt trời có thể đóng vai trò then chốt trong quản lý môi trường cũng như các lĩnh vực khác như năng lượng, nông nghiệp, và mô hình thủy văn, sinh thái. Tại một số khu vực, dữ liệu bức xạ mặt trời không đủ do thiếu thiết bị pyranometer hoặc thiết bị này thường xuyên bị hỏng. Do đó, việc có một bộ ước lượng để ước lượng bức xạ mặt trời dựa trên các biến khí hậu khác là rất quan trọng. Để phát triển một công cụ ước lượng, hai mô hình đã được áp dụng đồng thời như một mô hình lai mới để ước lượng bức xạ mặt trời toàn cầu hàng tháng cho ba khu vực ở Iran, làm nghiên cứu điển hình của công trình nghiên cứu này: (1) một mạng nơ-ron nhân tạo (ANN) được tối ưu hóa bằng thuật toán tối ưu hóa của chim diều hâu Harris (HHO) (ANNHHO) và (2) tái cấu trúc không gian pha (PSR) tích hợp với mô hình lai ANNHHO (PSR-ANNHHO). Dữ liệu khí tượng hàng tháng về nhiệt độ tối thiểu (Tmin), nhiệt độ tối đa (Tmax), nhiệt độ trung bình (Tmean), số giờ nắng (SH), tốc độ gió (U2) và độ ẩm tương đối (RH) trong 37 năm (1985–2018) từ ba khu vực ở Iran với các loại khí hậu khác nhau đã được sử dụng để đào tạo và kiểm tra các mô hình đã phát triển. Để chọn các biến đầu vào phù hợp cho các mô hình, một thuật toán relief đã được áp dụng. Hiệu suất của các mô hình lai mới được so sánh với mô hình ANN độc lập. Kết quả thu được cho thấy mặc dù tất cả các mô hình thông minh hoạt động thoả đáng, mô hình lai PSR-ANNHHO vượt trội so với mô hình lai ANNHHO và mô hình ANN độc lập ở tất cả các khu vực. Mô hình ANNHHO lai theo sau mô hình PSR-ANNHHO như một mô hình chính xác thứ hai.
Từ khóa
#bức xạ mặt trời #mô hình mạng nơ-ron nhân tạo #tối ưu hóa #tái cấu trúc không gian pha #khí tượng học #mô hình laiTài liệu tham khảo
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