Tên bài báo:
Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms- Tác giả:
- Đỗ Quang Hưng
- Tham gia cùng:
- Tạp chí:
- Information
- Năm xuất bản:
- 2017
- Trang:
- Từ trang 1 đến trang 15
- Lĩnh vực:
- Công nghệ thông tin
- Phạm vi:
- Quốc tế
Tóm tắt:
Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs) trained by different heuristic algorithms, including Gravitational Search Algorithm (GSA) and Cuckoo Optimization Algorithm (COA), are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods.
