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  • Open Access

    ARTICLE

    Application of Grey Model and Neural Network in Financial Revenue Forecast

    Yifu Sheng1, Jianjun Zhang1,*, Wenwu Tan1, Jiang Wu1, Haijun Lin1, Guang Sun2, Peng Guo3

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4043-4059, 2021, DOI:10.32604/cmc.2021.019900 - 24 August 2021

    Abstract There are many influencing factors of fiscal revenue, and traditional forecasting methods cannot handle the feature dimensions well, which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future trend. The grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using Lasso. It can reduce the dimensionality of the original data, make separate predictions for each explanatory variable, and then use neural networks to make multivariate predictions,… More >

  • Open Access

    ARTICLE

    Wind Power Revenue Potential: Simulation for Finland

    Sakarias Paaso*, Ali Khosravi

    Energy Engineering, Vol.118, No.4, pp. 1111-1133, 2021, DOI:10.32604/EE.2021.014949 - 31 May 2021

    Abstract Potential revenue from wind power generation is an important factor to be considered when planning a wind power investment. In the future, that may become even more important because it is known that wind power generation tends to push electricity wholesale prices lower. Consequently, it is possible that if a region has plenty of installed wind power capacity, revenue per generated unit of electricity is lower there than could be assumed by looking at the mean electricity wholesale price. In this paper, we compare 17 different locations in Finland in terms of revenue from wind More >

  • Open Access

    ARTICLE

    Resource Management in Cloud Computing with Optimal Pricing Policies

    Haiyang Zhang1, Guolong Chen2, Xianwei Li2,3,*

    Computer Systems Science and Engineering, Vol.34, No.4, pp. 249-254, 2019, DOI:10.32604/csse.2019.34.249

    Abstract As a new computing paradigm, cloud computing has received much attention from research and economics fields in recent years. Cloud resources can be priced according to several pricing options in cloud markets. Usage-based and reserved pricing schemes are commonly adopted by leading cloud service providers (CSPs) such as Amazon and Google. With more and more CSPs entering cloud computing markets, the pricing of cloud resources is an important issue that they need to consider. In this paper, we study how to segment cloud resources using hybrid pricing schemes in order to obtain the maximum revenue More >

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