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学者姓名：林培杰
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Abstract ：
为解决生成对抗网络训练过程中因损失简单加权导致的图像感知质量下降问题,提出损失自适应调整的生成对抗超分辨率网络(LA-GAN).首先,该方法设计通过计算角点分布的相关强度大小,区分规则纹理区域与不规则纹理区域.其次,基于不同区域,设计了区域自适应生成对抗学习框架.在该框架中,网络只在不规则纹理区域中进行对抗学习,提高感知质量.此外,基于下采样图像和图像块相似性的重组图像取代训练集中的高分辨率图像,实现平均绝对损失在不规则纹理区域弱约束网络,在规则纹理区域强约束网络,保证图像信号保真度.最后,通过实验证明经过优化的网络在信号保真度和感知质量方面皆有提升.
Keyword ：
区域自适应 区域自适应 损失函数 损失函数 生成对抗网络 生成对抗网络 超分辨率 超分辨率
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| GB/T 7714 | 林旭锋 , 吴丽君 , 陈志聪 et al. 损失自适应的高感知质量生成对抗超分辨率网络 [J]. | 福州大学学报（自然科学版） , 2025 , 53 (1) : 26-34 . |
| MLA | 林旭锋 et al. "损失自适应的高感知质量生成对抗超分辨率网络" . | 福州大学学报（自然科学版） 53 . 1 (2025) : 26-34 . |
| APA | 林旭锋 , 吴丽君 , 陈志聪 , 林培杰 , 程树英 . 损失自适应的高感知质量生成对抗超分辨率网络 . | 福州大学学报（自然科学版） , 2025 , 53 (1) , 26-34 . |
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Recently, promising progresses have been made in photovoltaic (PV) arrays fault diagnosis (FD) due to the importance of operation and maintenance of PV power plants. However, PV arrays inevitably experience gradual degradation due to the complexity of operating conditions, resulting in domain shift of output data, which has a significant negative impact on the performance of FD. To address these problems, this study proposes a two-stage cross-domain, i.e., adaptive generative adversarial network deep learning approach for PV arrays FD under different degradation levels. In the first stage, the Normal data from the source domain (PV arrays without performance degradation) is utilized for training. Then, the Maximum Mean Discrepancy (MMD) loss is introduced to the fault generators in adversarial training to produce high-level feature representations of source domain fault data. In the second stage, identical training steps are used to guide the fault generators. Specifically, Normal data from the target domain i.e., PV arrays with performance degradation, is utilized to generate fault data features that are consistent with the target domain features. Then, the cross-domain adaptive FD model can be trained by using generated fault data features. The proposed model can not only learn the relationship from the different types of data, but also utilize target domain PV array data under healthy conditions to manually generate fake samples for cross-domain adaptive FD. Experimental results show that the Precision of the proposed model in the two tasks is 98.34% and 92.93 %, with Recall is 98.23 % and 94.13%, F1-Score is 0.9823 and 0.9274, all of which are better than those of the comparison models.
Keyword ：
Adversarial networks Adversarial networks Domain adaptation Domain adaptation Fault diagnosis Fault diagnosis Generative models Generative models Photovoltaic arrays Photovoltaic arrays
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| GB/T 7714 | Lin, Peijie , Guo, Feng , Lin, Yaohai et al. Fault diagnosis of photovoltaic arrays with different degradation levels based on cross-domain adaptive generative adversarial network [J]. | APPLIED ENERGY , 2025 , 386 . |
| MLA | Lin, Peijie et al. "Fault diagnosis of photovoltaic arrays with different degradation levels based on cross-domain adaptive generative adversarial network" . | APPLIED ENERGY 386 (2025) . |
| APA | Lin, Peijie , Guo, Feng , Lin, Yaohai , Cheng, Shuying , Lu, Xiaoyang , Chen, Zhicong et al. Fault diagnosis of photovoltaic arrays with different degradation levels based on cross-domain adaptive generative adversarial network . | APPLIED ENERGY , 2025 , 386 . |
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Soiling can reduce the output power and work efficiency of photovoltaic (PV) modules, causing serious economic losses to PV systems. The cleaning schedules can be optimized to save economic expenses through the methods capable of estimating the power loss of PV modules resulting from soiling. This paper proposes a deep learning framework that combines visible light and infrared image information with dual branch cross-modality feature fusion. Initially, the MobileNetV2 is applied as the backbone of the dual branch framework to enhance the training efficiency and reduce the computational complexity. Subsequently, a cross-modality differential aware fusion module based on the channel attention mechanism (CA-CMDAF) is introduced to improve the crossmodality feature fusion capability of the model. Moreover, a multi-cascade and cross-modality fusion network and a multi-scale fusion network are integrated to further facilitate the effectiveness of feature fusion and reduce the loss of visual details during the feature extraction. Lastly, extensive experiments are carried out on the multimodality dataset. The comparison results demonstrate the superior performance of the proposed dual branch network framework with the average accuracy of 88.27 %, which is higher than that of the single-modality models trained on either visible light or infrared images alone.
Keyword ：
CA-CMDAF CA-CMDAF Deep learning Deep learning Multi-modality feature fusion Multi-modality feature fusion PV power generation PV power generation Soiling loss Soiling loss
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| GB/T 7714 | Lin, Peijie , Chen, Hang , Cheng, Shuying et al. Assessment of power loss caused by soiling PV modules using a dual branch multi-modality deep learning network framework [J]. | RENEWABLE ENERGY , 2025 , 248 . |
| MLA | Lin, Peijie et al. "Assessment of power loss caused by soiling PV modules using a dual branch multi-modality deep learning network framework" . | RENEWABLE ENERGY 248 (2025) . |
| APA | Lin, Peijie , Chen, Hang , Cheng, Shuying , Lu, Xiaoyang , Lin, Yaohai , Sun, Lei . Assessment of power loss caused by soiling PV modules using a dual branch multi-modality deep learning network framework . | RENEWABLE ENERGY , 2025 , 248 . |
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为提高户外光伏电站现场退化评估的准确性和可靠性,提出一种物理和数据驱动的光伏组件性能退化模型.研究户外光伏组件受静态温度、循环温度、相对湿度和紫外线影响的特性,并综合动态应力函数,利用累积损失模型对多应力下光伏电站性能退化进行建模.此外,退化模型的未知参数通过遗传算法来提取.使用美国国家太阳辐射数据库的长期数据对该模型进行训练和测试.将性能退化实际值和模型计算值进行对比,结果表明,本研究所提出模型的相对误差更低,验证了该方法的可行性.
Keyword ：
优化算法 优化算法 光伏电站 光伏电站 光伏退化 光伏退化 数据驱动 数据驱动
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| GB/T 7714 | 王宇钖 , 陈志聪 , 吴丽君 et al. 利用物理和数据驱动的光伏性能退化建模方法 [J]. | 福州大学学报（自然科学版） , 2024 , 52 (5) : 513-519 . |
| MLA | 王宇钖 et al. "利用物理和数据驱动的光伏性能退化建模方法" . | 福州大学学报（自然科学版） 52 . 5 (2024) : 513-519 . |
| APA | 王宇钖 , 陈志聪 , 吴丽君 , 俞金玲 , 程树英 , 林培杰 . 利用物理和数据驱动的光伏性能退化建模方法 . | 福州大学学报（自然科学版） , 2024 , 52 (5) , 513-519 . |
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提出一种基于图注意力网络(graph attention network,GAT)的光伏阵列故障诊断模型,以解决光伏阵列中因故障导致的发电效率降低、正常运行受阻等问题.通过离散小波变换和滑窗算法截取故障后稳态时序信号并将其分割成子区间,将子区间视为图节点.使用K邻近构图法将故障后稳态电压、电流数据转变成图结构,构建节点级GAT模型.通过多头注意力机制自动提取电压、电流图结构的故障特征.通过实验室光伏阵列获取实验数据集,对所提模型进行测试.结果表明,本模型能准确诊断光伏阵列的不同故障状态,平均准确率达到99.790％,效果优于所对比的其他网络模型.
Keyword ：
光伏阵列 光伏阵列 图神经网络 图神经网络 图结构 图结构 故障诊断 故障诊断 注意力机制 注意力机制
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| GB/T 7714 | 董浪灿 , 卢箫扬 , 林培杰 et al. 利用GAT的光伏阵列故障诊断方法 [J]. | 福州大学学报（自然科学版） , 2024 , 52 (5) : 505-512 . |
| MLA | 董浪灿 et al. "利用GAT的光伏阵列故障诊断方法" . | 福州大学学报（自然科学版） 52 . 5 (2024) : 505-512 . |
| APA | 董浪灿 , 卢箫扬 , 林培杰 , 程树英 , 陈志聪 , 吴丽君 . 利用GAT的光伏阵列故障诊断方法 . | 福州大学学报（自然科学版） , 2024 , 52 (5) , 505-512 . |
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提出一种基于高效通道注意(ECA)网络和双向长短期记忆神经网络(BILSTM)的自适应智慧路灯边缘计算模型.首先,在BILSTM的基础上,融合布谷鸟算法、通道注意力机制,构建CS-ECA-BILSTM能见度预测模型,实现道路能见度预测;其次,为普通路灯控制因子单一的问题引入照度和色温因子,提高控制效率并降低路灯能耗;最后,在边缘端引入云原生理念,使用KubeEdge框架与容器技术部署路灯控制模型到边缘端,从而实现多路灯控制.实验结果表明,所提出CS-ECA-BILSTM模型的性能优于其他对比模型,可有效提高路灯能源利用率,且能实现在边缘端的运行.
Keyword ：
双向长短期记忆神经网络 双向长短期记忆神经网络 容器技术 容器技术 智慧路灯 智慧路灯 注意力机制 注意力机制 边缘计算 边缘计算
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| GB/T 7714 | 郭泽鑫 , 林培杰 , 程树英 et al. 基于CS-ECA-BILSTM与KubeEdge的自适应智慧路灯边缘计算模型 [J]. | 福州大学学报（自然科学版） , 2024 , 52 (6) : 667-673 . |
| MLA | 郭泽鑫 et al. "基于CS-ECA-BILSTM与KubeEdge的自适应智慧路灯边缘计算模型" . | 福州大学学报（自然科学版） 52 . 6 (2024) : 667-673 . |
| APA | 郭泽鑫 , 林培杰 , 程树英 , 陈志聪 , 吴丽君 . 基于CS-ECA-BILSTM与KubeEdge的自适应智慧路灯边缘计算模型 . | 福州大学学报（自然科学版） , 2024 , 52 (6) , 667-673 . |
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为提高光伏发电的预测精确度,提出一种结合主成分分析(PCA)、双通道注意力(CSA)机制和Informer的短期光伏发电量预测新模型.采用Spearman相关分析方法对光伏发电的多元时间序列进行分析,并结合PCA提取时序特征,构建输入数据集.同时,引入CSA机制模块,提取光伏发电历史数据的时间维度和空间维度的特征,然后输入Informer模型进行预测.采用以 30 min为分辨率的光伏电站公开数据集进行实验验证和对比分析.实验结果表明,本研究所提出的预测模型在 4 步预测中的平均绝对误差为 0.061 5,均方误差为0.0205,均方根误差为 0.1435,R2 为 0.9872,均优于其他比较模型,有望为光伏短期发电量预测提供更好的预测精确度.
Keyword ：
Informer模型 Informer模型 主成分分析 主成分分析 光伏发电预测 光伏发电预测 双通道注意力机制 双通道注意力机制 短期发电量 短期发电量
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| GB/T 7714 | 蔡伟雄 , 陈志聪 , 吴丽君 et al. 采用PCA-CSA-Informer模型的光伏短期发电量预测 [J]. | 福州大学学报（自然科学版） , 2024 , 52 (6) : 681-690 . |
| MLA | 蔡伟雄 et al. "采用PCA-CSA-Informer模型的光伏短期发电量预测" . | 福州大学学报（自然科学版） 52 . 6 (2024) : 681-690 . |
| APA | 蔡伟雄 , 陈志聪 , 吴丽君 , 程树英 , 林培杰 . 采用PCA-CSA-Informer模型的光伏短期发电量预测 . | 福州大学学报（自然科学版） , 2024 , 52 (6) , 681-690 . |
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Short-term water demand forecasting is essential for ensuring the sustainable use of water resources. The accuracy of water demand forecasting directly impacts the rationality of water resources management and the effectiveness of scheduling. Therefore, it is vital to accurately forecast water demand across various timescales. Based on this motivation, we propose an improved patch time series Transformer (PatchTST) model to forecast the multi-timescale short-term water demand. By introducing relative positional encoding (RPE), the model effectively learns the relationships between tokens. The model combines the global token information capture ability of the self-attention mechanism with the local token information capture ability of the convolutional network to enhance feature extraction abilities. Additionally, the model integrates the advantages of patch-wise and series-wise representation, enabling it to simultaneously capture both local and global dependencies in time series. We utilize historical data collected from district metering area to experimentally validate the effectiveness of the proposed model. Compared with one-dimensional convolutional neural network (1D-CNN), long shortterm memory (LSTM), Transformer, DLinear, and PatchTST models, our model demonstrates superior performance across all five forecasting scales. Finally, the effectiveness of the proposed design is further validated through ablation experiments.
Keyword ：
Deep learning Deep learning Multi-timescale Multi-timescale PatchTST PatchTST Water demand forecasting Water demand forecasting
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| GB/T 7714 | Lin, Peijie , Zhang, Xiangxin , Gong, Longcong et al. Multi-timescale short-term urban water demand forecasting based on an improved PatchTST model [J]. | JOURNAL OF HYDROLOGY , 2024 , 651 . |
| MLA | Lin, Peijie et al. "Multi-timescale short-term urban water demand forecasting based on an improved PatchTST model" . | JOURNAL OF HYDROLOGY 651 (2024) . |
| APA | Lin, Peijie , Zhang, Xiangxin , Gong, Longcong , Lin, Jingwei , Zhang, Jie , Cheng, Shuying . Multi-timescale short-term urban water demand forecasting based on an improved PatchTST model . | JOURNAL OF HYDROLOGY , 2024 , 651 . |
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针对传统参数辨识方法中存在的易陷入局部最优和精度低问题,提出一种改进洗牌复杂演化算法(shuffed complex evolution,SCE).首先,提出描述电池的动态特性的二阶RC等效电路模型,并根据恒流放电工况测试数据集进行锂离子电池等效模型确定待辨识参数.其次,将模型模拟端电压值与电池真实测试端电压均方根误差作为目标函数,并通过所提出的优化算法来寻找模型最优参数.最后,使用DST、FUDS的锂离子电池动态工况数据集进行仿真验证,并与粒子群算法、灰狼算法、遗传算法进行比较.仿真结果表明,本方法在辨识精度方面具有优势,算法的参数辨识均方根误差(ERMS)平均值是0.0166V,相比较其他优化算法,分别降低了 7.8％、8.3％、14.9％.
Keyword ：
参数辨识 参数辨识 洗牌复杂演化算法 洗牌复杂演化算法 等效电路模型 等效电路模型 锂离子电池 锂离子电池
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| GB/T 7714 | 许雅玲 , 陈志聪 , 吴丽君 et al. 利用改进SCE算法的锂离子电池参数辨识 [J]. | 福州大学学报（自然科学版） , 2024 , 52 (2) : 147-154 . |
| MLA | 许雅玲 et al. "利用改进SCE算法的锂离子电池参数辨识" . | 福州大学学报（自然科学版） 52 . 2 (2024) : 147-154 . |
| APA | 许雅玲 , 陈志聪 , 吴丽君 , 林培杰 , 程树英 . 利用改进SCE算法的锂离子电池参数辨识 . | 福州大学学报（自然科学版） , 2024 , 52 (2) , 147-154 . |
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针对目前大部分光伏功率预测模型采用批量离线训练方式,且新建光伏电站训练数据较少的问题,本文提出一种基于增量学习的卷积神经网络(CNN)和长短期记忆(LSTM)网络结合的光伏功率预测模型.首先,采用CNN对气象数据进行特征提取,并通过LSTM网络进行功率预测,以此CNN-LSTM混合模型进行背景学习,训练出可用于增量学习的基准模型.其次,根据不同的时间跨度进行增量学习训练,实现模型的在线更新.针对增量学习中的灾难性遗忘问题,采用弹性权重整合(EWC)算法和在线弹性整合(Online_EWC)算法进行缓解.实验结果表明,相较于无约束的增量学习,采用EWC和Online_EWC方法的增量学习可以明显缓解灾难性遗忘问题,降低预测平均绝对误差(MAE)和均方根误差(RMSE);同时,在保证预测精度的前提下,增量学习的耗时大幅低于传统的批量学习.
Keyword ：
光伏功率预测 光伏功率预测 增量学习 增量学习 弹性权重整合(EWC)算法 弹性权重整合(EWC)算法 长短期记忆(LSTM)网络 长短期记忆(LSTM)网络
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| GB/T 7714 | 严璐晗 , 林培杰 , 程树英 et al. 基于增量学习的CNN-LSTM光伏功率预测 [J]. | 电气技术 , 2024 , 25 (5) : 31-40 . |
| MLA | 严璐晗 et al. "基于增量学习的CNN-LSTM光伏功率预测" . | 电气技术 25 . 5 (2024) : 31-40 . |
| APA | 严璐晗 , 林培杰 , 程树英 , 陈志聪 , 卢箫扬 . 基于增量学习的CNN-LSTM光伏功率预测 . | 电气技术 , 2024 , 25 (5) , 31-40 . |
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