Simulation and forecasting of fishery weather based on statistical machine learning

As the new generation of artificial intelligence(AI)continues to evolve,weather big data and statistical machine learning(SML)technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery weather.Accurate fishery weather services play a crucial role in fishery production,serving as a great safeguard for economic benefits and personal safety,enabling fishermen to carry out fishery production better,and contributing to the sustainable development of the fishery industry.The objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery weather.Specifically,we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather,which mainly include three aspects:fishery weather scenario generation,fishery weather forecasting,and fishery extreme weather warning.We also illustrate the main technical means and principles of SML technology.Finally,we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.

Statistical machine learning model for capacitor planning considering uncertainties in photovoltaic power

New energy integration and flexible demand response make smart grid operation scenarios complex and change-able,which bring challenges to network planning.If every possible scenario is considered,the solution to the plan-ning can become extremely time-consuming and difficult.This paper introduces statistical machine learning(SML)techniques to carry out multi-scenario based probabilistic power flow calculations and describes their application to the stochastic planning of distribution networks.The proposed SML includes linear regression,probability distribu-tion,Markov chain,isoprobabilistic transformation,maximum likelihood estimator,stochastic response surface and center point method.Based on the above SML model,capricious weather,photovoltaic power generation,thermal load,power flow and uncertainty programming are simulated.Taking a 33-bus distribution system as an example,this paper compares the stochastic planning model based on SML with the traditional models published in the literature.The results verify that the proposed model greatly improves planning performance while meeting accuracy require-ments.The case study also considers a realistic power distribution system operating under stressed conditions.

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

The development of distributed renewable energy,such as photovoltaic power and wind power generation,makes the energy system cleaner,and is of great significance in reducing carbon emissions.However,weather can affect distributed renewable energy power generation,and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy.Energy systems with high penetration of distributed renewable energy involve the high-dimensional,nonlinear dynamics of large-scale complex systems,and the optimal solution of the uncertainty model is a difficult problem.From the perspective of statistical machine learning,the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.

Online payment fraud:from anomaly detection to risk management

Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.

On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data

Abstract The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. We use a nonparametric approach based on a combination of kernel logistic regression and e-support vector regression which both have good robustness properties. The strategy is applied to a data set from motor vehicle insurance companies.