An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting

With the popularity of Internet of things technology and intelligent devices, the application prospect of accurate step counting has gained more and more attention. To solve the problems that the existing algorithms use threshold to filter noise, and the parameters cannot be updated in time, an intelligent optimization strategy based on deep reinforcement learning is proposed. In this study, the counting problem is transformed into a serialization decision optimization. This study integrates the noise recognition and the user feedback to update parameters. The end-to-end processing is direct, which alleviates the inaccuracy of step counting in the follow-up step counting module caused by the inaccuracy of noise filtering in the two-stage processing and makes the model parameters continuously updated. Finally, the experimental results show that the proposed model achieves superior performance to existing approaches.

A Clustering-based Method for Business Hall Efficiency Analysis

Excessive or insufficient business hall resources may result in unreasonable resource allocation, adversely affecting the value of an entity business hall. Therefore, proper characteristic parameters are the key factors for analyzing the business hall, which strongly affect the final analysis results. In this study, a characteristic analysis method for the economic operation of a business hall is developed and the feature engineering is established. Because of its simplicity and versatility, the k -means algorithm has been widely used since it was first proposed around 50 years ago. However, the classical k -means algorithm has poor stability and accuracy. In particular, it is difficult to achieve a suitable balance between of the centroid initialization and the clustering number k . We propose a new initialization (LSH- k -means) algorithm for k -means clustering. This algorithms is mainly based on locality-sensitive hashing (LSH) as an index for computing the initial cluster centroids, and it reduces the range of the clustering number. Furthermore, an empirical study is conducted. According to the load intensity and time change of the business hall, an index system reflecting the optimization analysis of the business hall is established, and the LSH- k -means algorithm is used to analyze the economic operation of the business hall. The results of the empirical study show that the LSH- k -means that the clustering method outperforms the direct prediction method, provides expected analysis results as well as decision optimization recommendations for the business hall, and serves as a basis for the optimal layout of the business hall.

Research on Decision Optimization Model of Microgrid Participating in Spot Market Transaction

The development of electricity spot trading provides an opportunity for microgrids to participate in the spot market transaction, which is of great significance to the research of microgrids participating in the electricity spot market. Under the background of spot market construction, this paper takes the microgrid including wind power, photovoltaic (PV), gas turbine, battery storage, and demand response as the research object, uses the stochastic optimization method to deal with the uncertainty of wind and PV power, and constructs a decision optimization model with the goal of maximizing the expected revenue of microgrids in the spot market. Through the case study, the optimal bidding electricity of microgrid operators in the spot market is obtained, and the revenue is USD 923.07. Then, this paper further investigates the effects of demand response, meteorological factors, market price coefficients, and cost coefficients on the expected revenue of microgrids. The results demonstrate that the demand response adopted in this paper has better social–economic benefits, which can reduce the peak load while ensuring the reliability of the microgrid, and the optimization model also ensure profits while extreme weather and related economic coefficients change, providing a set of scientific quantitative analysis tools for microgrids to trade electricity in the spot market.

A Computational Approach to Sequential Decision Optimization in Energy Storage and Trading

Due to recent technical progress, battery energy storages are becoming a viable option in the power sector. Their optimal operational management focuses on load shift and shaving of price spikes. However, this requires optimally responding to electricity demand, intermittent generation, and volatile electricity prices. More importantly, such optimization must take into account the so-called deep discharge costs, which have a significant impact on battery lifespan. We present a solution to a class of stochastic optimal control problems associated with these applications. Our numerical techniques are based on efficient algorithms which deliver a guaranteed accuracy.