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The volatility of solar energy, geographic location, and weather factors continues to affect the stability of photovoltaic power generation, reliable and accurate photovoltaic power prediction methods not only effectively reduce the operating cost of the photovoltaic system but also provide reliable data support for the energy scheduling of the light storage microgrid, improve the stability of the photovoltaic system, and provide important help for the optimization operation of the photovoltaic system. Therefore, it is an important study to find reliable photovoltaic power prediction methods. In recent years, researchers have improved the accuracy of photovoltaic power generation forecasting by using deep learning models. Compared with the traditional neural network, the Transformer model can better learn the relationship between weather features and has good stability and applicability. Therefore, in this paper, the transformer model is used for predicting ultra-short-term photovoltaic power generation, and the photovoltaic power generation data and weather data in Hebei are selected. In the experiment, the prediction result of the transformer model was compared to the GRU and DNN models to show that the transformer model has better predictive ability and stability. Experimental results demonstrated that the proposed Transformer model outperforms the GRU model and DNN model by a difference of about 0.04 kW and 0.047 kW in the MSE value, and 22.0% and 29.1% of the MAPE error. In addition, the public DC competition dataset is selected for control experiments to demonstrate the general applicability of the transformer model for PV power prediction in different regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • In the case of a small number of known deposits and mineral occurrences, the prospecting prediction model based on the Siamese network can extract effective features without relying on prior knowledge to realize supervised prospecting prediction. • On the basis of sliding window data enhancement, this paper generates paired sample data sets by randomly combining deposit samples and non-deposit samples, which can further enhance the data and improve the generalization ability and prediction accuracy of the model to a certain extent. • Compared with the prediction results of the weight of evidence method and the convolutional neural network, the prediction results of the Siamese network are better, and 7 new gold prospecting areas have been discovered in Zhangjiakou, Hebei Province, China. Supervised neural networks constitute an important research area for the intelligent prediction of locations for prospecting mineral deposits. Accurate training of a supervised neural network requires many training samples, something that is often difficult to obtain. This paper reports the use of geological, geochemical, and geophysical data by a Siamese network to overcome the problem of insufficient training samples, and implements a supervised deep learning prospecting prediction method based on the Siamese network. Intelligent prediction for gold deposits prospecting is carried out for the Chongli mineral concentrate area in Zhangjiakou, Hebei Province, China, and compared with the weight of evidence method and convolutional neural network model. The results show that:(a) the performance of the Siamese network model is no less than that of the convolutional neural network (CNN) model and better than that of the weight of evidence (WOE) method; (b) the gold prospective areas differentiated by the established models are strongly consistent with geological and metallogenic characteristics in the study area. This study suggests Siamese network model as an effective mineral prospectivity modeling tool. This method is also suitable for prospecting prediction using geoscience data in other areas. [ABSTRACT FROM AUTHOR]

Increasing electricity demand gives a rise to the necessity of cleaner and safer electric supply. Short-term-related time series prediction in electricity power system with high precision is irreplaceable in the efficient management of electric systems. However, it is both a challenging and significant task to achieve the accurate prediction of short-term-related time series. Many models lack stability and ignore the importance of meteorological factors, which lead to poor prediction accuracy. This paper develops a reliable numerical model for verification based on fuzzy inference and meteorological factors (atmospheric temperature, atmospheric pressure and atmospheric density). The fuzzy neural networks are a favourable scheme in electricity power system time series predictions mainly due to their endogenous capacity of robust modelling of data sets with highly non-linear relationship between inputs and outputs. Three experiments covering the data collected from Hebei are performed to verify the effectiveness of the proposed hybrid model by comparing it with three well-known methods. It is concluded that the hybrid models proposed not only can satisfactorily approximate the actual value but they also can be an effective tool in the planning and dispatching of smart grids. [ABSTRACT FROM AUTHOR]

The green growth of Beijing-Tianjin-Hebei (BTH) urban agglomeration plays a leading and exemplary role in overcoming internal resource restrictions, addressing climate change, and supporting China's high-quality growth. From the standpoint of pollution reduction and carbon reduction, this paper first conducts a comprehensive evaluation of the environmental impact based on combined weighting technique. The Logarithmic Mean Divisia Index (LMDI) model is used to decompose the environmental impact drivers in distinct areas. A decoupling effort index is further constructed to measure the effect of various efforts on the decoupling of economic growth and environmental impact, the improved grey Markov model is applied to predict the future trend of regional decoupling efforts. The results of empirical analysis based on data of the BTH region during 2011–2018 show that: 1) the environmental impact index of Beijing is the lowest followed by Tianjin and Hebei; 2) environmental regulation exerts the most significant impact on reducing environmental pressure in Beijing while technology progress and energy intensity have the most significant effect on easing environmental pressure in Tianjin; 3) strong decoupling efforts have been found in Beijing, Tianjin and Hebei, however, such effect is more significant in Beijing; 4) Beijing's decoupling state is mostly driven by regulatory effect, intensity effect, and scale effect, while Tianjin and Hebei's decoupling states are primarily driven by improvements in environmental regulation and energy intensity; 5) according to the forecast outcome of the improved grey Markov technique, a state of strong decoupling effort will be maintained in the BTH area by 2025, and the decoupling effort index in Beijing will remain the highest while the index in Hebei will remain the lowest. • A comprehensive evaluation of the environmental impact of BTH based on combined weighting method is estimated. • Determinants of environmental impact is decomposed as regulatory, technological, intensity, growth and scale effects. • LMDI model is constructed to estimate the five determinants of regional environmental impact. • Decoupling effort approach and improved grey Markov model are used to analyze and predict the status of various decoupling efforts. • Various suggestions are proposed from the perspective of regional heterogeneity. [ABSTRACT FROM AUTHOR]

A forecast approach of water structure based on GM (1, 1) of the gray system is proposed. Based on economic and water information of Hebei Province from 2000 to 2018, the water use structure of Hebei's industrial sector form 2019 to 2030 is forecasted according to the composition data and gray system GM (1, 1) model. The forecasting results by the proposed approach shows that the water structure of the tertiary industry has changed from 62.8 : 10.3 : 26.9 in 2018 to 60.5 : 10.2 : 29.3 in 2030. The proportion of water used in the primary and secondary industries has decreased slightly, the proportion of water used in the tertiary industry has increased, and the proportion of water used in the tertiary industry has not changed significantly. [ABSTRACT FROM AUTHOR]