Journal of Energy Science and Technology is an international, peer-reviewed open access journal dedicated to advancing research the field of energy science and technology. The journal provides a rapid publication process to ensure wide dissemination of high-quality articles to scientists, professionals, and interested individuals worldwide. Our goal is to serve as an efficient, reliable, and trusted platform for scholars and readers, publishing cutting-edge research in the field.
Abstract: The prediction of solar irradiance plays a crucial role in the optimization operation of multi energy collaborative heating systems. As a key factor affecting the efficiency of the heating system, the accuracy of solar irradiance prediction is directly related to the operational efficiency and energy consumption of the heating system. However, solar irradiance has uncertain characteristics such as intermittency and nonlinearity in time and space, which poses challenges for existing machine learning prediction algorithms in hyperparameter optimization and makes it difficult to meet optimization and regulation requirements. To address this issue, we propose a solar irradiance prediction model based on Whale Algorithm Optimization Time Convolutional Network (WOA-TCN). This model fully utilizes the parallel processing capability of TCN time convolutional networks and the temporal modeling function of recurrent neural networks. The hyperparameters of the TCN are optimized by a swarm intelligence optimization algorithm that simulates the behavior of whales, which improves the prediction accuracy of the model, enables the temporal and spatial evolution patterns of solar irradiance to be captured more accurately, and also provides strong technical support for the optimal operation of the multi-energy cooperative heating system. The prediction indexes of CNN, RNN and other algorithms are also compared through experiments, and the results show that the prediction accuracy of the solar irradiance prediction model based on WOA-TCN is higher.Abstract: The prediction of solar irradiance plays a crucial role in the optimization operation of multi energy collaborative heating systems. As a key factor affecting the efficiency of the heating system, the accuracy of solar irradiance prediction is directly related to the operational efficiency and energy consumption of the heating system. However, sola...Learn More
Abstract: Power transformer is the core equipment in the power system, and its health status concerns the safe and stable operation of power system. Accurately evaluating and reliably predicting on the health status and lifespan of the transformer can provide important information for transformer operation and maintenance, consequently reduce the cost and fault risk. This work proposes a multidimensional degradation fusion method for evaluating the health status and predict the lifespan of transformers. Firstly, the multidimensional indicators that affect the health status of transformers are analyzed, and thus an indicator system is established. The degradation of each indicator is quantified by calculating the deviation between the measurement value and the initial value. Secondly, we analyzed impacts of various indicators on the health of transformers, hence we obtained the contribution weights of each dimension of deterioration on the health of transformers. Furthermore, we considered the nonlinear impact of indicator deterioration changes on transformer health. Consequently, we established the transformer health assessment model and the lifespan prediction model based on multidimensional deterioration nonlinear fusion mapping. Finally, we took an example to evaluate the health status of a transformer by utilizing the established mathematical model. The results demonstrated that the evaluation results of this method were consistent with reality, and the model was effective.Abstract: Power transformer is the core equipment in the power system, and its health status concerns the safe and stable operation of power system. Accurately evaluating and reliably predicting on the health status and lifespan of the transformer can provide important information for transformer operation and maintenance, consequently reduce the cost and fa...Learn More
