{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:16:07Z","timestamp":1759331767497,"version":"3.40.5"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:00:00Z","timestamp":1673568000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yunnan Power Grid Technology Project","award":["YNKJXM20210141"],"award-info":[{"award-number":["YNKJXM20210141"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2023,1,13]]},"abstract":"<jats:p>Power load forecasting (PLF) has a positive impact on the stability of power systems and can reduce the cost of power generation enterprises. To improve the forecasting accuracy, more information besides load data is necessary. In recent years, a novel privacy-preserving paradigm vertical federated learning (FL) has been applied to PLF to improve forecasting accuracy while keeping different organizations\u2019 data locally. However, two problems are still not well solved in vertical FL. The first problem is a lack of a full data-processing procedure, and the second is a lack of enhanced privacy protection for data processing. To address it, according to the procedure in a practical scenario, we propose a vertical FL XGBoost-based PLF, where multiparty secure computation is used to enhance the privacy protection of FL. Concretely, we design a full data-processing PLF, including data cleaning, private set intersection, feature selection, federated XGBoost training, and inference. Furthermore, we further use RSA encryption in the private set intersection and Paillier homomorphic encryption in the training and inference phases. To validate the proposed method, we conducted experiments to compare centralized learning and vertical FL on several real-world datasets. The proposed method can also be directly applied to other practical vertical FL tasks.<\/jats:p>","DOI":"10.1155\/2023\/9914169","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T14:08:43Z","timestamp":1673618923000},"page":"1-9","source":"Crossref","is-referenced-by-count":4,"title":["Full Data-Processing Power Load Forecasting Based on Vertical Federated Learning"],"prefix":"10.1155","volume":"2023","author":[{"given":"Zhengxiong","family":"Mao","sequence":"first","affiliation":[{"name":"Network Information Center, Yunnan Power Grid Company Limited, Kunming, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"Network Information Center, Yunnan Power Grid Company Limited, Kunming, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuyuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Network Information Center, Yunnan Power Grid Company Limited, Kunming, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Tian","sequence":"additional","affiliation":[{"name":"Network Information Center, Yunnan Power Grid Company Limited, Kunming, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7033-9315","authenticated-orcid":true,"given":"Peng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","unstructured":"ChenJ.Application of grey theory and artificial neural network in medium- and long-term load forecasting of power system2019Jiang Su Sheng, ChinaSuzhou UniversityMaster\u2019s thesis"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2017.08.009"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3071269"},{"key":"4","first-page":"65","article-title":"Survey on data aggregation and privacy protection of user query in smart grid","volume":"11","author":"K. Li","year":"2021","journal-title":"Netinfo Security"},{"issue":"4","key":"5","first-page":"1","article-title":"Xgboost: extreme gradient boosting","volume":"1","author":"T. Chen","year":"2015","journal-title":"R package version 0.4-2"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/mis.2021.3082561"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/PTC.2005.4524543"},{"key":"8","first-page":"108","article-title":"Summer short-term load forecasting based on ARIMAX model","volume":"4","author":"H. Cui","year":"2015","journal-title":"Power System Protection and Control"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrs.2018.2872388"},{"issue":"11","key":"10","first-page":"42","article-title":"Short-term power load forecasting based on deep forest algorithm","volume":"39","author":"L. 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