Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions.Energy industries worldwide are trying hard to predict ...Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions.Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing.Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies.The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data.Another critical factor is balancing the data for enhanced prediction.Data Augmentation is a technique used for increasing the data available for training.Synthetic data are the generation of new data which can be trained to improve the accuracy of prediction models.In this paper,we propose a model that takes time series energy consumption data as input,pre-processes the data,and then uses multiple augmentation techniques and generative adversarial networks to generate synthetic data which when combined with the original data,reduces energy consumption prediction error.We propose TGAN-skip-Improved-WGAN-GP to generate synthetic energy consumption time series tabular data.We modify TGANwith skip connections,then improveWGANGPby defining a consistency term,and finally use the architecture of improved WGAN-GP for training TGAN-skip.We used various evaluation metrics and visual representation to compare the performance of our proposed model.We also measured prediction accuracy along with mean and maximum error generated while predicting with different variations of augmented and synthetic data with original data.The mode collapse problemcould be handled by TGAN-skip-Improved-WGAN-GP model and it also converged faster than existing GAN models for synthetic data generation.The experiment result shows that our proposed technique of combining synthetic data with original data could significantly reduce the prediction error rate and increase the prediction accuracy of energy consumption.展开更多
Data gathering in wireless sensor networks is one of the important operations in these networks. These operations require energy consumption. Due to the limited energy of nodes, the energy productivity should be consi...Data gathering in wireless sensor networks is one of the important operations in these networks. These operations require energy consumption. Due to the limited energy of nodes, the energy productivity should be considered as a key objective in design of sensor networks. Therefore the clustering is a suitable method that used in energy consumption management. For this purpose many methods have been proposed. Between these methods the LEACH algorithm has been attend as a basic method. This algorithm uses distributed clustering method for data gathering and aggregation. The LEACH-C method that is the improvement of LEACH, which performs the clustering in centralized mode. In this method, collecting the energy level of information of every node directly in each period increases the energy cost. Also the phenomenon that is seen by sensor nodes continually change over time. Thereby the information received by nodes is correlated. Sending time correlated data in the network cause to energy dissipation. TINA method and its improvement have been proposed in order to not sending correlated data. These approaches have reported errors. In this paper, the idea of not sending time correlated data of nodes has been considered by using the time series function. Also, a model to estimate the remaining energy of nodes by the base station has been presented. Finally, a method has been proposed to aware the base station from the number of correlated data in each node as the estimation of energy will be more precise. The proposed ideas have been implemented over the LEACH-C protocol. Evaluation results showed that the proposed methods had a better performance in energy consumption and the lifetime of the network in comparison with similar methods.展开更多
Due to the development of diversified and flexible building energy resources,the balancing energy supply and demand especially in smart build-ings caused an increasing problem.Energy forecasting is necessary to addres...Due to the development of diversified and flexible building energy resources,the balancing energy supply and demand especially in smart build-ings caused an increasing problem.Energy forecasting is necessary to address building energy issues and comfort challenges that drive urbanization and consequent increases in energy consumption.Recently,their management has a great significance as resources become scarcer and their emissions increase.In this article,we propose an intelligent energy forecasting method based on hybrid deep learning,in which the data collected by the smart home through meters is put into the pre-evaluation step.Next,the refined data is the input of a Long Short-Term Memory(LSTM)network,which captures the spatio-temporal correlations from the sequence and generates the feature maps.The output feature map is passed into a Deep Extreme Machine Learning network(with seven hidden layers)for learning,which provides the final prediction.Compared to existing techniques,the LSTM-DELM model offers better prediction results.The simulation values demonstrate the superior performance of the proposed model.展开更多
Nowadays, the deep learning methods are widely applied to analyze and predict the trend of various disaster events and offer the alternatives to make the appropriate decisions. These support the water resource managem...Nowadays, the deep learning methods are widely applied to analyze and predict the trend of various disaster events and offer the alternatives to make the appropriate decisions. These support the water resource management and the short-term planning. In this paper, the water levels of the Pattani River in the Southern of Thailand have been predicted every hour of 7 days forecast. Time Series Transformer and Linear Regression were applied in this work. The results of both were the water levels forecast that had the high accuracy. Moreover, the water levels forecasting dashboard was developed for using to monitor the water levels at the Pattani River as well.展开更多
对于用能数据不足的综合能源系统,借助相似系统的丰富数据可以为其建立高精度的多元负荷预测模型,然而,受数据安全等因素的限制,很多系统并不愿意共享自身数据。联邦学习为处理隐私保护下的少数据综合能源多元负荷预测问题提供了一个重...对于用能数据不足的综合能源系统,借助相似系统的丰富数据可以为其建立高精度的多元负荷预测模型,然而,受数据安全等因素的限制,很多系统并不愿意共享自身数据。联邦学习为处理隐私保护下的少数据综合能源多元负荷预测问题提供了一个重要的思路,但是现有方法依然存在相似参与方识别精度不高等不足。鉴于此,本文提出一种融合联邦学习和长短期记忆网络(long short-term memory,LSTM)的少数据综合能源多元负荷预测方法(multitask learning based on shared dot product confidentiality under federated learning,MT-SDPFL)。首先,给出一种基于共享向量点积保密协议的相似参与方识别方法,用来从诸多可用的综合能源系统中选出最为相似的参与方;接着,使用参数共享联邦学习算法对选中的各参与方联合训练,结合LSTM和finetune技术建立每个参与方的多元负荷预测模型。将所提方法应用于多个实际能源系统,实验结果表明,该方法可以在数据稀疏的情况下取得高精度的多源负荷预测结果。展开更多
为提高风电机组系统的运行效率和稳定性,提出一种基于数据驱动的风电机组能效状态分析方法,实现对机组异常状态的高效检测与预警。首先从损耗与效率的角度出发,开展各因素对机组能效的影响分析,考虑到系统各部件能量损失主要体现在热量...为提高风电机组系统的运行效率和稳定性,提出一种基于数据驱动的风电机组能效状态分析方法,实现对机组异常状态的高效检测与预警。首先从损耗与效率的角度出发,开展各因素对机组能效的影响分析,考虑到系统各部件能量损失主要体现在热量方面,因此以温度参数为依据,建立基于能量流的能效状态指标体系。然后利用风电机组数据采集与监视控制(supervisory control and data acquisition,SCADA)系统采集数据,确定各参数基准区间,构建指标偏离度矩阵,利用改进的鲸鱼算法(improved whale optimization algorithm,IWOA)优化支持向量机,实现对能效异常状态的检测。同时引入能效异常指数来表征机组能效变化情况,利用自回归滑动平均模型-支持向量机(autoregressive moving average model-support vector machines,ARMA-SVM)组合模型实现能效的时间序列预测。最后以1.5 MW双馈异步风电机组为研究对象开展算例分析。结果表明该方法能够实现对能效异常状态的有效检测和预警,为风电机组的性能优化与故障分析提供了必要的决策参考。展开更多
基金This research was financially supported by the Ministry of Small and Mediumsized Enterprises(SMEs)and Startups(MSS),Korea,under the“Regional Specialized Industry Development Program(R&D,S3091627)”supervised by Korea Institute for Advancement of Technology(KIAT).
文摘Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions.Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing.Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies.The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data.Another critical factor is balancing the data for enhanced prediction.Data Augmentation is a technique used for increasing the data available for training.Synthetic data are the generation of new data which can be trained to improve the accuracy of prediction models.In this paper,we propose a model that takes time series energy consumption data as input,pre-processes the data,and then uses multiple augmentation techniques and generative adversarial networks to generate synthetic data which when combined with the original data,reduces energy consumption prediction error.We propose TGAN-skip-Improved-WGAN-GP to generate synthetic energy consumption time series tabular data.We modify TGANwith skip connections,then improveWGANGPby defining a consistency term,and finally use the architecture of improved WGAN-GP for training TGAN-skip.We used various evaluation metrics and visual representation to compare the performance of our proposed model.We also measured prediction accuracy along with mean and maximum error generated while predicting with different variations of augmented and synthetic data with original data.The mode collapse problemcould be handled by TGAN-skip-Improved-WGAN-GP model and it also converged faster than existing GAN models for synthetic data generation.The experiment result shows that our proposed technique of combining synthetic data with original data could significantly reduce the prediction error rate and increase the prediction accuracy of energy consumption.
文摘Data gathering in wireless sensor networks is one of the important operations in these networks. These operations require energy consumption. Due to the limited energy of nodes, the energy productivity should be considered as a key objective in design of sensor networks. Therefore the clustering is a suitable method that used in energy consumption management. For this purpose many methods have been proposed. Between these methods the LEACH algorithm has been attend as a basic method. This algorithm uses distributed clustering method for data gathering and aggregation. The LEACH-C method that is the improvement of LEACH, which performs the clustering in centralized mode. In this method, collecting the energy level of information of every node directly in each period increases the energy cost. Also the phenomenon that is seen by sensor nodes continually change over time. Thereby the information received by nodes is correlated. Sending time correlated data in the network cause to energy dissipation. TINA method and its improvement have been proposed in order to not sending correlated data. These approaches have reported errors. In this paper, the idea of not sending time correlated data of nodes has been considered by using the time series function. Also, a model to estimate the remaining energy of nodes by the base station has been presented. Finally, a method has been proposed to aware the base station from the number of correlated data in each node as the estimation of energy will be more precise. The proposed ideas have been implemented over the LEACH-C protocol. Evaluation results showed that the proposed methods had a better performance in energy consumption and the lifetime of the network in comparison with similar methods.
文摘Due to the development of diversified and flexible building energy resources,the balancing energy supply and demand especially in smart build-ings caused an increasing problem.Energy forecasting is necessary to address building energy issues and comfort challenges that drive urbanization and consequent increases in energy consumption.Recently,their management has a great significance as resources become scarcer and their emissions increase.In this article,we propose an intelligent energy forecasting method based on hybrid deep learning,in which the data collected by the smart home through meters is put into the pre-evaluation step.Next,the refined data is the input of a Long Short-Term Memory(LSTM)network,which captures the spatio-temporal correlations from the sequence and generates the feature maps.The output feature map is passed into a Deep Extreme Machine Learning network(with seven hidden layers)for learning,which provides the final prediction.Compared to existing techniques,the LSTM-DELM model offers better prediction results.The simulation values demonstrate the superior performance of the proposed model.
文摘Nowadays, the deep learning methods are widely applied to analyze and predict the trend of various disaster events and offer the alternatives to make the appropriate decisions. These support the water resource management and the short-term planning. In this paper, the water levels of the Pattani River in the Southern of Thailand have been predicted every hour of 7 days forecast. Time Series Transformer and Linear Regression were applied in this work. The results of both were the water levels forecast that had the high accuracy. Moreover, the water levels forecasting dashboard was developed for using to monitor the water levels at the Pattani River as well.
文摘对于用能数据不足的综合能源系统,借助相似系统的丰富数据可以为其建立高精度的多元负荷预测模型,然而,受数据安全等因素的限制,很多系统并不愿意共享自身数据。联邦学习为处理隐私保护下的少数据综合能源多元负荷预测问题提供了一个重要的思路,但是现有方法依然存在相似参与方识别精度不高等不足。鉴于此,本文提出一种融合联邦学习和长短期记忆网络(long short-term memory,LSTM)的少数据综合能源多元负荷预测方法(multitask learning based on shared dot product confidentiality under federated learning,MT-SDPFL)。首先,给出一种基于共享向量点积保密协议的相似参与方识别方法,用来从诸多可用的综合能源系统中选出最为相似的参与方;接着,使用参数共享联邦学习算法对选中的各参与方联合训练,结合LSTM和finetune技术建立每个参与方的多元负荷预测模型。将所提方法应用于多个实际能源系统,实验结果表明,该方法可以在数据稀疏的情况下取得高精度的多源负荷预测结果。
文摘为提高风电机组系统的运行效率和稳定性,提出一种基于数据驱动的风电机组能效状态分析方法,实现对机组异常状态的高效检测与预警。首先从损耗与效率的角度出发,开展各因素对机组能效的影响分析,考虑到系统各部件能量损失主要体现在热量方面,因此以温度参数为依据,建立基于能量流的能效状态指标体系。然后利用风电机组数据采集与监视控制(supervisory control and data acquisition,SCADA)系统采集数据,确定各参数基准区间,构建指标偏离度矩阵,利用改进的鲸鱼算法(improved whale optimization algorithm,IWOA)优化支持向量机,实现对能效异常状态的检测。同时引入能效异常指数来表征机组能效变化情况,利用自回归滑动平均模型-支持向量机(autoregressive moving average model-support vector machines,ARMA-SVM)组合模型实现能效的时间序列预测。最后以1.5 MW双馈异步风电机组为研究对象开展算例分析。结果表明该方法能够实现对能效异常状态的有效检测和预警,为风电机组的性能优化与故障分析提供了必要的决策参考。
