The ability to operate across multiple timescales is vital for the flexibility enhancement of integrated electricity and heating systems (IEHS). Nonetheless, the common dependence on numerical solvers operating in discrete spaces poses a challenge in accurately representing their inherent multi-timescale properties, thereby compromising system security. In response, this paper proposes a novel multi-timescale matching method in continuous space to get rid of resolution constraints, circumventing discretization issues while deriving an explicit formulation for thermal dynamics. This forms the groundwork for establishing the global sensitivity factors to quantify the interdependencies within the IEHS. On this basis, a rolling security evaluation and regulation strategy that is cognizant of the asynchronous behaviors in power and heating systems is tailored. Case studies validate the effectiveness and efficiency of the proposed method to ensure the IEHS’s security.

Pest management has long been a critical aspect of crop protection. Insect behavior is of great research value as an important indicator for assessing insect characteristics. Currently, insect behavior research is increasingly based on the quantification of behavior. Traditional manual observation and analysis methods can no longer meet the requirements of data volume and observation time. In this paper, we propose a method based on region localization combined with an improved 3D convolutional neural network for six grooming behaviors of Bactrocera minax: head grooming, foreleg grooming, fore-mid leg grooming, mid-hind leg grooming, hind leg grooming, and wing grooming. The overall recognition accuracy reached 93.46%. We compared the results obtained from the detection model with manual observations; the average difference was about 12%. This shows that the model reached a level close to manual observation. Additionally, recognition time using this method is only one-third of that required for manual observation, making it suitable for real-time detection needs. Experimental data demonstrate that this method effectively eliminates the interference caused by the walking behavior of Bactrocera minax, enabling efficient and automated detection of grooming behavior. Consequently, it offers a convenient means of studying pest characteristics in the field of crop protection.

In recent years, due to the rapid growth of new energy installed capacity, the electricity consumption situation is getting tougher. In view of the serious wind abandonment phenomenon in northern China during the winter heating season, this paper investigates the operating mechanism of combined electric storage heaters with wind power consumption in electricity market. Considering the effects of operating power of electric storage heaters and the positive and negative reserve capacities, this paper proposes a revenue distribution strategy for the electric storage heaters when participating in spot market transactions with new energy generators. And the effectiveness of the strategy is verified by a case.

An Efficient Unification-Based Multimodal Language Processor for Multimodal Input Fusion: 10.4018/978-1-60566-386-9.ch004: Multimodal user interaction technology aims at building natural and intuitive interfaces allowing a user to interact with computers in a way similar to

The short-term probabilistic prediction of wind power has the characteristics of spatial dependence and time-series dependence. Considering the two characteristics at the same time can improve the prediction level. In this paper, a probabilistic short-term wind power prediction model considering the temporal and spatial dependence of prediction error is proposed. Considering the coupling relationship between the two properties, the NWP(Numerical Weather Prediction) wind speed point prediction error in the historical period is hierarchical clustering, and the empirical distribution model is used to fit the probability distribution of the error under different wind conditions; the cumulative empirical distribution probability value corresponding to the NWP wind speed at the time to be predicted is bootstrap sampling; under the given confidence level, the possible wind speed at each time point to be predicted in the short term is calculated The power fluctuation range of the generator. The test results show that this method can ensure the statistical significance and fitting stability of the sub sample set at the same time, and improve the classification accuracy of the days to be predicted. Compared with considering a single property, the result of probability prediction performs better on multiple evaluation indexes.