[1] SRINIVASA K T,RITESH P E,HARISH V S K,et al. A comprehensive study on the recent progress and trends in development of small hydropower projects[J].Energies,2021,14(10):2882. [2] 陈辉, 王文烨, 熊龙珠, 等. 小水电退网对地区电网潮流分布的影响[J]. 湖南电力, 2020,40(6):12-17. [3] 刘本希, 廖胜利, 冯仲恺,等. 耦合偏互信息的贫资料地区小水电发电能力预测[J]. 电力系统自动化,2015,39(19):149-154. [4] 阳勇乐. 流域水电站运行优化探讨[J]. 水电科技, 2023,6(2): 116-119. [5] TIAN Y Z,ZHANG F,YUAN Z, et al. Assessment power generation potential of small hydropower plants using GIS software[J]. Energy Reports, 2020,6:1393-1404. [6] 赵泽谦, 黄强, 明波, 等. 基于多模型随机组合的水文集合预报方法研究[J]. 水力发电学报, 2021, 40(1): 76-87. [7] MEHR A D,GHIASI A R,YASEEN Z M, et al. A novel intelligent deep learning predictive model for meteorological drought forecasting[J]. Journal of Ambient Intelligence and Humanized Computing, 2023,14(8):10441-10455. [8] DENG H Q,CHEN W J,HUANG G R.Deep insight into daily runoff forecasting based on a CNN-LSTM model[J]. Natural Hazards, 2022, 113(3): 1675-1696. [9] HAMMID A T,BIN Sulaiman M H, ABDALLA A N. Prediction of small hydropower plant power production in Himreen Lake dam (HLD) using artificial neural network[J]. Alexandria Engineering Journal, 2018, 57(1): 211-221. [10] MENDES R C F, MAC D R R, MIRANDA A R S, et al. Monitoring a hydrokinetic converter system for remaining energy in hydropower plants[J]. IEEE Latin America Transactions, 2020, 18(10): 1683-1691. [11] LE DEUNF J, DEBESE N, SCHMITT T, et al. A review of data cleaning approaches in a hydrographic framework with a focus on bathymetric multibeam echosounder datasets[J]. Geosciences, 2020, 10(7): 254. [12] BAI S,LI M C,KONG R, et al. Data mining approach to construction productivity prediction for cutter suction dredgers[J]. Automation in Construction, 2019, 105: 102833. [13] 蒋云钟, 冶运涛, 赵红莉, 等. 水利大数据研究现状与展望[J]. 水力发电学报, 2020, 39(10): 1-32. [14] YE S J, WANG C, WANG Y L, et al. Real-time model predictive control study of run-of-river hydropower plants with data-driven and physics-based coupled model[J]. Journal of Hydrology, 2023, 617: 128942. [15] BAPTISTA J, FARIA P, CANIZES B, et al. Power quality of renewable energy source systems: a new paradigm of electrical grids[J]. Energies, 2022, 15(9): 3195. [16] WEI Z T, SHEN X D, QIU G, et al. An integrated transfer learning method for power generation prediction of run-off small hydropower in data-scarce areas[J]. IEEE Transactions on Smart Grid,2023,PP(99):1. [17] WONG T T, YEH P Y. Reliable accuracy estimates from k-fold cross validation[J]. IEEE Transactions on Knowledge and Data Engineering, 2020,32(8):1586-1594. [18] YANG S J, WEI H A, ZHANG L, et al. Daily power generation forecasting method for a group of small hydropower stations considering the spatial and temporal distribution of precipitation:south china case study[J]. Energies, 2021, 14(15): 4387. [19] JUNG J,HAN H,KIM K, et al. Machine learning-based small hydropower potential prediction under climate change[J]. Energies, 2021, 14(12): 3643. [20] SAKKI G K, TSOUKALAS I, EFSTRATIADIS A. A reverse engineering approach across small hydropower plants: a hidden treasure of hydrological data?[J]. Hydrological Sciences Journal,2022,67(1):94-106. |