Since wave fluctuates continuously, the forecast of energy is very important for operation power systems integrated with large-scale generation. A combined model day-ahead based on improved grey BP neural network (BPNN) and modified ensemble empirical mode decomposition (MEEMD) -autoregressive moving average (ARIMA) proposed in this paper. Firstly, decomposed into wind waves swells by theories. Secondly, correlation between speed analyzed BPNN, height can be forecasted historical data....

In the post-pandemic era, host cities are increasingly utilizing sports events as strategic tools to enhance brand identity, stimulate urban development, and drive economic recovery. This paper investigates 2022 Hangzhou Asian Games a case study in city branding, analyzing effectiveness of two strategies: cost-efficient infrastructure development integration cultural heritage with innovative technology. Through content stakeholder analyses, research discusses Games' impact on various...

Knowledge distillation~(KD) aims to craft a compact student model that imitates the behavior of pre-trained teacher in target domain. Prior KD approaches, despite their gratifying results, have largely relied on premise \emph{in-domain} data is available carry out knowledge transfer. Such an assumption, unfortunately, many cases violates practical setting, since original training or even domain often unreachable due privacy copyright reasons. In this paper, we attempt tackle ambitious task,...

Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data augmentation, anomaly detection, and privacy preservation. Although significant strides have been made this field, existing methods exhibit three key limitations: (1) They often benchmark against similar model types, constraining holistic view performance capabilities. (2) The use specialized synthetic private datasets introduces biases hampers generalizability. (3) Ambiguous evaluation measures,...

Abstract This paper proposes a modified iterative learning control (MILC) periodical feedback-feedforward algorithm to reduce the vibration of rotor caused by coupled unbalance and parallel misalignment. The is provided an active magnetic actuator (AMA). gain MILC here presented has self-adjustment based on magnitude vibration. Notch filters are adopted extract synchronous (1 × Ω ) twice rotational frequency (2 components Both notch filter size feedforward storage used during experiment have...

With the improvement of level grid intelligence, distribution network, as terminal power system, ensures safety room and has an important significance to smooth operation grid. To be able understand condition secondary equipment in real-time ensure safe stable room, a method for sensing status is proposed. Firstly, noise processing based on wavelet transform proposed environmental data such voltage current then time-frequency analysis characteristics designed denoised transformed into form...

Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data augmentation, anomaly detection, and privacy preservation. Although significant strides have been made this field, existing methods exhibit three key limitations: (1) They often benchmark against similar model types, constraining holistic view performance capabilities. (2) The use specialized synthetic private datasets introduces biases hampers generalizability. (3) Ambiguous evaluation measures,...

Application of fuzzy support vector machine in stock price forecast.Support is a new type learning method proposed 1990s.It can deal with classification and regression problems very successfully.Due to the excellent performance machine, technology has become hot research topic field learning, it been successfully applied many fields.However, as technology, there are limitations machines.There large amount information objective world.If training contains noise information, will weak...

Time Series Generation (TSG) has emerged as a pivotal technique in synthesizing data that accurately mirrors real-world time series, becoming indispensable numerous applications. Despite significant advancements TSG, its efficacy frequently hinges on having large training datasets. This dependency presents substantial challenge data-scarce scenarios, especially when dealing with rare or unique conditions. To confront these challenges, we explore new problem of Controllable (CTSG), aiming to...

Electric power companies often need to deal with a large amount of data, which are stored in the data centre stage is extremely dispersed, and problem difficult fetch use arises. This paper proposes an intelligent recommendation algorithm based on electric data. Firstly, constructed business logic relationship mapping, this calculates similarity between demand information content, carry out correlation analysis cross-departmental cross-professional obtain collection department where located...

Time Series Generation (TSG) is essential in many industries for generating synthetic data that mirrors real-world characteristics. TSGBench has advanced the field by offering comprehensive evaluations and unique insights selecting suitable TSG methods. However, translating these advancements to industry applications hindered a cognitive gap among professionals absence of dynamic platform method comparison evaluation. To address issues, we introduce TSGAssist, an interactive assistant...

Concentrating solar power plant (CSP) generation is a latest approach of utilizing resources to generate electricity in recent years, which has considerable development prospect. The CSP with thermal energy storage (TES) system features good dispatchability and operation flexibility, beneficial for reducing fluctuation output renewable generation. mechanism analyzed, the dispatching model TES also formulated. integrated photovoltaic wind improve multi-energy schedulability, an optimal...

Application of fuzzy support vector machine in stock price forecast. Support is a new type learning method proposed 1990s. It can deal with classification and regression problems very successfully. Due to the excellent performance machine, technology has become hot research topic field learning, it been successfully applied many fields. However, as technology, there are limitations machines. There large amount information objective world. If training contains noise information, will weak...

The uncertainty and fluctuation characteristics of photovoltaic power pose great challenges to the safe operation peak-shaving process systems. Since cascade hydropower plants have flexible rapid regulating capacity, a short-term model for coordinated hydro system (CHPVS) is proposed. Then, proposed transformed into mixed integer linear programming (MILP) problem, solution presented. Finally, case study demonstrates effective peak- shaving CHPVS, feasibility practicability are also validated.