Ứng dụng mô hình kết hợp GCN-Wavenet trong dự báo tải ngắn hạn cho hệ thống lưới điện nhỏ
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Author
Từ khóa:
Mạng tích chập đồ thị (GCN)
Wavenet
phụ tải ngắn hạn (STLF)
Mạng nơ ron tích chập truyền thống (CNN)
Tóm tắt
Dự báo phụ tải điện là một vấn đề quan trọng trong quản lý năng lượng lưới điện nhỏ (Microgrid - MG). Dự báo phụ tải với việc xem xét nhiều yếu tố tác động để nâng cao độ chính xác và đáp ứng cho những biến động của các yếu tố đó là vấn đề đang được quan tâm trong MG. Bài báo này đề xuất một phương pháp tích hợp mới để dự báo phụ tải ngắn hạn (STLF); Xem xét sử dụng cả chuỗi dữ liệu dài và ngắn của phụ tải và một số yếu tố như công suất đỉnh, nhiệt độ,… để dự báo nhu cầu tải hàng giờ của MG. Nhóm tác giả xem xét một mô hình dự đoán với nhiều yếu tố, nghiên cứu này đã tích hợp Mạng tích chập đồ thị (Graph Convolutional Network - GCN) vào các nút của mạng Wavenet. Mô hình dự báo được so sánh với các mô hình dự báo trước đó. Kết quả cho thấy, mô hình đề xuất của nhóm tác giả vượt trội hơn các mô hình dựa trên học sâu khác về RMSE và MAPE.
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Nov 30, 2022
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Nguyễn Thanh Hoan, Lê Duy Phúc, Trương Việt Anh, Nguyễn Hữu Vinh, Trương Đình Nhơn, và Lê Kim Hùng. “Ứng dụng Mô hình kết hợp GCN-Wavenet Trong dự báo tải ngắn hạn Cho hệ thống lưới điện Nhỏ”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 20, số p.h 11.2, Tháng Mười-Một 2022, tr 136-41, https://jst-ud.vn/jst-ud/article/view/8031.
