TY - GEN
T1 - Federated Rank Learning with Dimensionality Reduction and Clustering for Electricity Load Forecasting
AU - Li, Lei
AU - Su, Bing
AU - Zhang, Shichao
AU - Liu, Yuchong
AU - Zheng, Jianchao
AU - Zhang, Chuan
AU - Zhu, Liehuang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Power load forecasting is crucial for power companies’ planning and power dispatching. With the development of machine learning, power forecasting has adopted artificial intelligence techniques based on machine learning. In this paper, we propose a novel forecasting scheme, FRLDRC, which combines the UMAP dimensionality reduction method, the K-means clustering algorithm, and ranking-based federated learning techniques. This approach allows us to obtain a forecasting model while ensuring data privacy, as the data does not leave its domain. To validate the effectiveness of the proposed model, we design experiments using over two million real household electricity consumption data points spanning four years. The experimental results demonstrate that data clustering with dimensionality reduction improves the performance of the baseline model. Additionally, the federated learning-based approach ensures data security, and the ranking federated technique further reduces communication overhead.
AB - Power load forecasting is crucial for power companies’ planning and power dispatching. With the development of machine learning, power forecasting has adopted artificial intelligence techniques based on machine learning. In this paper, we propose a novel forecasting scheme, FRLDRC, which combines the UMAP dimensionality reduction method, the K-means clustering algorithm, and ranking-based federated learning techniques. This approach allows us to obtain a forecasting model while ensuring data privacy, as the data does not leave its domain. To validate the effectiveness of the proposed model, we design experiments using over two million real household electricity consumption data points spanning four years. The experimental results demonstrate that data clustering with dimensionality reduction improves the performance of the baseline model. Additionally, the federated learning-based approach ensures data security, and the ranking federated technique further reduces communication overhead.
KW - Clustering
KW - Dimensionality Reduction
KW - Electricity Load Forecasting
KW - Federated Rank Learning
KW - K-means
UR - https://www.scopus.com/pages/publications/105023153541
U2 - 10.1007/978-981-95-3061-8_6
DO - 10.1007/978-981-95-3061-8_6
M3 - Conference contribution
AN - SCOPUS:105023153541
SN - 9789819530601
T3 - Lecture Notes in Computer Science
SP - 46
EP - 58
BT - Knowledge Science, Engineering and Management - 18th International Conference, KSEM 2025, Proceedings
A2 - Zhu, Tianqing
A2 - Zhou, Wanlei
A2 - Zhu, Congcong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Knowledge Science, Engineering and Management, KSEM 2025
Y2 - 4 August 2025 through 7 August 2025
ER -