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  • Open Access

    ARTICLE

    Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

    Reem Ibrahim Alkanhel1, Doaa Sami Khafaga2, Ahmed Mohamed Zaki3, Marwa M. Eid4,5, Abdyalaziz A. Al-Mooneam6, Abdelhameed Ibrahim7, S. K. Towfek3,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3549-3568, 2024, DOI:10.32604/cmc.2024.049582 - 20 June 2024

    Abstract The networks of wireless sensors provide the ground for a range of applications, including environmental monitoring and industrial operations. Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization. Network infrastructure planning should be focused on increasing performance, and it should be affected by the detailed data about node distribution. This work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location, which will contribute to better network planning and design. By using the ARIMA model for time… More >

  • Open Access

    ARTICLE

    An Accurate Dynamic Forecast of Photovoltaic Energy Generation

    Anoir Souissi1,*, Imen Guidara1, Maher Chaabene1, Giuseppe Marco Tina2, Moez Bouchouicha3

    FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.6, pp. 1683-1698, 2022, DOI:10.32604/fdmp.2022.022051 - 27 June 2022

    Abstract The accurate forecast of the photovoltaic generation (PVG) process is essential to develop optimum installation sizing and pragmatic energy planning and management. This paper proposes a PVG forecast model for a PVG/Battery installation. The forecasting strategy is built on a Medium-Term Energy Forecasting (MTEF) approach refined dynamically every hour (Dynamic Medium-Term Energy Forecasting (DMTEF)) and adjusted by means of a Short-Term Energy Forecasting (STEF) strategy. The MTEF predicts the generated energy for a day ahead based on the PVG of the last 15 days. As for STEF, it is a combination between PVG Short-Term (ST) More >

  • Open Access

    ARTICLE

    Forecasting Model Based on Information-Granulated GA-SVR and ARIMA for Producer Price Index

    Xiangyan Tang1,2, Liang Wang3, Jieren Cheng1,2,4,*, Jing Chen2, Victor S. Sheng5

    CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 463-491, 2019, DOI:10.32604/cmc.2019.03816

    Abstract The accuracy of predicting the Producer Price Index (PPI) plays an indispensable role in government economic work. However, it is difficult to forecast the PPI. In our research, we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA (Autoregressive Integrated Moving Average Model) models. The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation. The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI, and produced three different sequences of fuzzy information granules, whose… More >

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