Short-term Load Prediction Based on User Behaviors Analysis

Efficient energy management is crucial given 2024's 4.1% worldwide electricity demand increase. This urgency emphasizes the necessity for various, sustainable energy sources in distribution grids. Short-term load prediction approaches using probabilistic power generation and energy storage are crucial for energy usage prediction. Urban energy planners use simulation, probability optimization and modelling to create sustainable energy systems. This study offers a novel hybrid model for smart grids: short-term energy load prediction using transfer learning (TL) and optimized lightGBM (OLGBM). Our two-phase solution tackles Short-term Load Forecasting complexities. First, aberrant supplements and quick deviation selection eliminate missing values and identify key features during data pre-processing. Second, TL-OLGBM learns dynamic time scales and complex data patterns with Bayesian optimization of hyperparameters to improve forecasting accuracy. Additionally, our architecture easily combines the newest Smart and Green Technology, enabling energy system innovation. Comparative performance research shows that our technique outperforms similar models in mean absolute percentage error, accuracy and root mean square error. This hybrid model is a reliable short-term energy load forecast solution that fits the dynamic terrain of smart and green technology integration in modern energy systems.

The first new species of Leuctridae (Plecoptera) from Jiangsu Province of eastern coastal China, Rhopalopsole vespertilio sp. nov. is described and illustrated. The new species belongs to the R. magnicerca species group and is distinguished by the bat-like shaped central plate of tergum 10, by the medially narrowed subtrapezoidal transverse bars, and by the upcurved hook formed by the small epiproct. The new species is compared with similar taxa.

An improved self-organizing incremental neural network model for short-term time-series load prediction

In this paper we develop an agent-based model to explore a feasible way of simultaneously providing sufficient food and bioenergy feedstocks in China. Concerns over the competition for agricultural land resources between food and bioenergy supply hinder the further development of bioenergy, especially in China, the country that needs to feed the world’s largest population. Prior research has suggested the introduction of energy crops and reviewed the resulting agricultural land use change in China. However, there is a lack of quantitative studies which estimate the value, contribution, and impact of bioenergy for specific conditions at the county level and provide adequate information to guide local practices. To fill this gap, we choose the Jiangsu Province in China as a case study, build up a spatial-agent dynamic model of agricultural land use, and perform a sensitivity analysis for important parameters. The simulation results show that straw from conventional crops generally dominates Jiangsu’s biomass supply with a contribution above 85%. The sensitivity analyses reveal severe consequences of bioenergy targets for local land use. For Jiangsu Province, reclaimed mudflats, an alternative to arable lands for energy crop plantation, help to secure the local biomass supply and to alleviate the land use conflict between food and biomass production.

The DEA-Malmquist model was used to measure the ecotourism efficiency of 13 cities in Jiangsu Province from 2010 to 2020, and the spatial and temporal differences were further analysed. The results show that the tourism technical efficiency in Jiangsu Province in China is generally at a high level, and the tourism scale efficiency restricts the improvement of tourism technical efficiency. The unbalanced of the technical progress in tourism between the prefecture-level cities is obvious. Overall, the change of technical progress in tourism has the strongest impact on the total factor productivity index. Therefore, strengthening the inter-regional cooperation of tourism development, training of tourism professionals, improving the structure of tourism industry, optimizing the allocation of tourism production factors, and expanding the scale and agglomeration effect of tourism can improve the efficiency of ecotourism.

Load-Frequency Control with Shortterm Load Prediction

Load prediction is crucial to maintaining a stable power grid and encouraging the use of innovative energy types like scenescape energy. The industrial load of a region accounts for a significant proportion of the total load, but the industrial load is difficult to predict due to its large fluctuations and instability. This study proposes a short-term power load prediction model, merging the Particle Swarm Optimization (PSO) algorithm and Long Short-Term Memory (LSTM) neural network. The PSO algorithm fine-tunes the LSTM network’s hyperparameters, which are then implemented on the LSTM layer. The trained model, using real-world datasets, is then tested for accuracy. Our model’s effectiveness is backed by practical examples.

Accurate cooling load prediction is essential for energy-efficient HVAC system operation. However, the stochastic and nonlinear nature of load data challenges conventional neural networks, causing prediction delays and errors. To address this, a novel hybrid model is developed. The approach first applies a two-stage decomposition (CEEMDAN with K-means and VMD) to process complex cooling load data. Then, a CNN-BiLSTM network optimized by the Crested Porcupine Optimizer and integrated with an attention mechanism is constructed for prediction. Experimental results demonstrate the model’s high performance, achieving a 96.75% prediction accuracy with a MAPE of 3.25% and an R2 of 0.9929. The proposed model shows strong robustness and generalization, providing a reliable reference for intelligent building energy management.

In order to achieve the short-term and high-precision port food transportation port food transportation mooring load prediction, a new principle and method of ship’s port food transportation port food transportation mooring load measurements based on indirect measurement is presented in this study and an algorithm is proposed through which predictions are made by comb the wavelet multi-scale decomposition and reconstruction method. Simulation results show that the combined algorithm has achieved short-term and high-precision port food transportation port food transportation mooring load prediction results, that it has excellent subdivision and self- learning abilities and that it can meet the accuracy requirement of the port food transportation port food transportation mooring load prediction in engineering.

Metabolic syndrome, a complex group of metabolic disorders of energy use and storage, is considered as an important determinant risk factor for many cardiovascular diseases. This study aimed to examine the association between metabolic syndrome (MetS) and dietary pattern among adults in Jiangsu Province of China. Data were from three rounds of cross–sectional nutrition and diet investigation projects in Jiangsu Province of China, which were conducted in 2002, 2007, and 2014 by Jiangsu Provincial Center for Disease Control and Prevention. A total of 13,944 participants with complete food frequency questionnaire (FFQ) were eventually included in this study after further data screening. The 2009 Joint Interim Statement for China was used to define metabolic syndrome. Three distinct dietary patterns were identified by factor analysis: the modern dietary pattern (rich in pork, poultry, vegetables, seafood, pastry food, other animal meats, fruits, milk and its products, soft drink, whole grains, nuts, and seeds, but low in wheat), vegetable oils/condiments/soy products dietary pattern (rich in vegetable oils, other condiments, salt, soy products, and fruits and low in dry legumes), and modern high–wheat dietary pattern (rich in wheat, tubers, fruits, and other animal meats, but low in rice). Higher intake of the modern dietary pattern and modern high–wheat dietary pattern were positively associated with metabolic syndrome in both unadjusted and adjusted models by genders, whereas higher intake of the vegetable oils/condiments/soy products dietary pattern had a negative relationship with metabolic syndrome in both unadjusted and adjusted models by genders (p < 0.05). Our study recommends reducing the consumption of animal meat products, especially processed meat products, and replacing animal oils with vegetable oils as the main supply of daily oils. Furthermore, more prospective and experimental studies are needed to confirm the relationship between dietary patterns and metabolic syndrome.

Stable ties have been formed between Russia and China in the field of tourism. Jiangsu Province in China is characterized by a high level of development in the recreational sector for travelers and is rich in cultural and tourist resources. In 2021, the region set the goal of becoming a “world-renowned tourist destination”, meaning a sought-after destination for travelers from various countries. While developing in this regard, the province has demonstrated significant growth in the number of accepted guests from other countries in recent years, however, Russians among them constitute a minority. The article aims to examine the tourist motivation of Russian travelers when visiting China and identify the cultural and resource advantages of Jiangsu Province that could attract them to the region. Three aspects of Russians’ travel preferences are noted – the unique culture of the destination, budget-friendly shopping and high comfort for vacationers. It is concluded that adapting the tourism industry of Jiangsu Province to cater to the preferences of Russian tourists presents an opportunity to increase tourist arrivals from Russia. Strategies focusing on promoting cultural attractions, enhancing shopping experiences, and improving leisure facilities can contribute to attracting more Russian tourists to the region, thereby bolstering tourism growth and economic development.

Objective: The primary objective of this study is to explore the effects of the lighting level on nighttime safety of signalized intersections based on conflict models under traffic conditions varying in cycles.Method: Nighttime data were collected from a field study at six signalized intersections in Nanjing, Jiangsu Province in China. Nighttime rear-end conflict models were developed by adopting the generalized linear model (GLM) approach to relate the frequency of rear-end traffic conflicts to lighting level, traffic volume and platoon ratio at the signal cycle level.Results: The final model consisting of all explanatory variables, including lighting level, traffic volume, and platoon ratio demonstrates a better performance of safety evaluation when compared to the model considering traffic volume only and the model with traffic volume and an additional variable of lighting or traffic conditions. Nighttime safety of signalized intersections is expected to improve with larger platoon ratios and higher lighting levels. A potential application of the final model was further explored by benefit-cost analyses. The analyses provided a hypothetical recommended lighting level under various traffic volumes and platoon ratios when safety benefit equals lighting system cost.Conclusions: Nighttime safety can be evaluated using the developed rear-end conflict models, which relate the number of rear-end conflicts to traffic and lighting variables. The number of rear-end conflicts can be calculated by the final conflict model with lighting level, traffic volume, and platoon ratio. The developed model can be potentially applied to provide further insights on the lighting management for intersection safety optimization with traffic conditions varying in signal cycles via vehicle-to-infrastructure (V2I) communications.

Carbon capability of urban residents and its structure: Evidence from a survey of Jiangsu Province in China

Street ballet in the context of population contraction: A case study of Dongda Street in Rugao City

It has been shown that land use change in urbanized region, especially urban land expansion, will influence regional climate. However, there has been very little research on the climate effects of the future land use change in a rapidly urbanizing region. Taking the southern part of Jiangsu province in China as the study area and through a scenario analysis, the influence of land use change on the regional temperature was analyzed from the perspective of land surface radiation budget and energy balance. The results indicated that (1) the monthly average temperature is significantly higher under the Rapid Economic Growth (REG) scenario than under the Cooperate Environmental Sustainability (CES) scenario in 2050, especially in the hottest month (July). (2) The range of high-temperature regions is much wider under the REG scenario than it is under the CES scenario in 2050. (3) The land surface net radiation and latent heat flux are two key factors through which land use change influences the regional temperature in the study area, and the latent heat flux plays a dominant role. (4) Land use change mainly influences the land surface net radiation by altering the land surface albedo and emissivity. These results are helpful to mitigate regional climate change effects caused by land use change.