Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (153)
  • Open Access

    ARTICLE

    An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

    Meilin Wu1,2, Lianggui Tang1,2,*, Qingda Zhang1,2, Ke Yan1,2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 179-198, 2023, DOI:10.32604/iasc.2023.036684

    Abstract As COVID-19 poses a major threat to people’s health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, and the features may then… More >

  • Open Access

    ARTICLE

    Long-Term Energy Forecasting System Based on LSTM and Deep Extreme Machine Learning

    Cherifa Nakkach*, Amira Zrelli, Tahar Ezzedine

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 545-560, 2023, DOI:10.32604/iasc.2023.036385

    Abstract Due to the development of diversified and flexible building energy resources, the balancing energy supply and demand especially in smart buildings caused an increasing problem. Energy forecasting is necessary to address building energy issues and comfort challenges that drive urbanization and consequent increases in energy consumption. Recently, their management has a great significance as resources become scarcer and their emissions increase. In this article, we propose an intelligent energy forecasting method based on hybrid deep learning, in which the data collected by the smart home through meters is put into the pre-evaluation step. Next, the refined data is the input… More >

  • Open Access

    ARTICLE

    Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed

    Neelam Mughees1,2, Mujtaba Hussain Jaffery1, Abdullah Mughees3, Anam Mughees4, Krzysztof Ejsmont5,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6375-6393, 2023, DOI:10.32604/cmc.2023.038564

    Abstract Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050. However, they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions. In microgrids, smart energy management systems, such as integrated demand response programs, are permanently established on a step-ahead basis, which means that accurate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids. With this in mind, a novel “bidirectional long short-term memory network” (Bi-LSTM)-based, deep stacked, sequence-to-sequence autoencoder (S2SAE) forecasting model… More >

  • Open Access

    ARTICLE

    Graph Construction Method for GNN-Based Multivariate Time-Series Forecasting

    Wonyong Chung, Jaeuk Moon, Dongjun Kim, Eenjun Hwang*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5817-5836, 2023, DOI:10.32604/cmc.2023.036830

    Abstract Multivariate time-series forecasting (MTSF) plays an important role in diverse real-world applications. To achieve better accuracy in MTSF, time-series patterns in each variable and interrelationship patterns between variables should be considered together. Recently, graph neural networks (GNNs) has gained much attention as they can learn both patterns using a graph. For accurate forecasting through GNN, a well-defined graph is required. However, existing GNNs have limitations in reflecting the spectral similarity and time delay between nodes, and consider all nodes with the same weight when constructing graph. In this paper, we propose a novel graph construction method that solves aforementioned limitations.… More >

  • Open Access

    ARTICLE

    Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

    Zeyu Wu1, Bo Sun1,2, Qiang Feng2,*, Zili Wang1, Junlin Pan1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 527-554, 2023, DOI:10.32604/cmes.2023.027124

    Abstract Due to the high inherent uncertainty of renewable energy, probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities. However, the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data. This article proposes a physics-informed artificial intelligence (AI) surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance. The incomplete dataset, built with numerical weather prediction data, historical wind power generation, and weather factors data, is augmented based on generative… More > Graphic Abstract

    Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

  • Open Access

    ARTICLE

    Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection

    You Lu1,2,#,*, Linqian Cui1,2,#,*, Yunzhe Wang1,2, Jiacheng Sun1,2, Lanhui Liu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 717-732, 2023, DOI:10.32604/cmes.2023.027032

    Abstract Most studies have conducted experiments on predicting energy consumption by integrating data for model training. However, the process of centralizing data can cause problems of data leakage. Meanwhile, many laws and regulations on data security and privacy have been enacted, making it difficult to centralize data, which can lead to a data silo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework. However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg) method is used to directly weight the model parameters on average, which may have an adverse effect on… More >

  • Open Access

    ARTICLE

    Improvements in Weather Forecasting Technique Using Cognitive Internet of Things

    Kaushlendra Yadav*, Anuj Singh, Arvind Kumar Tiwari

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3767-3782, 2023, DOI:10.32604/csse.2023.033991

    Abstract Forecasting the weather is a challenging task for human beings because of the unpredictable nature of the climate. However, effective forecasting is vital for the general growth of a country due to the significance of weather forecasting in science and technology. The primary motivation behind this work is to achieve a higher level of forecasting accuracy to avoid any damage. Currently, most weather forecasting work is based on initially observed numerical weather data that cannot fully cover the changing essence of the atmosphere. In this work, sensors are used to collect real-time data for a particular location to capture the… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems

    Firas Abedi1, Hayder M. A. Ghanimi2, Mohammed A. M. Sadeeq3, Ahmed Alkhayyat4,*, Zahraa H. Kareem5, Sarmad Nozad Mahmood6, Ali Hashim Abbas7, Ali S. Abosinnee8, Waleed Khaild Al-Azzawi9, Mustafa Musa Jaber10,11, Mohammed Dauwed12

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3359-3374, 2023, DOI:10.32604/cmc.2023.034221

    Abstract Recent economic growth and development have considerably raised energy consumption over the globe. Electric load prediction approaches become essential for effective planning, decision-making, and contract evaluation of the power systems. In order to achieve effective forecasting outcomes with minimum computation time, this study develops an improved whale optimization with deep learning enabled load prediction (IWO-DLELP) scheme for energy storage systems (ESS) in smart grid platform. The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS. The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and… More >

  • Open Access

    ARTICLE

    PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

    Yiqun Zhu1, Shuxian Sun1, Chunyu Liu1, Xinyi Tian1, Jingyi He2, Shuai Xiao2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3285-3297, 2023, DOI:10.32604/cmes.2023.026691

    Abstract With the rapid development of artificial intelligence and computer technology, grid corporations have also begun to move towards comprehensive intelligence and informatization. However, data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data. The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’ needs and their habits, providing better services for users. Nevertheless, users’ electricity consumption data is sensitive and private. In order to achieve highly efficient analysis of massive private electricity consumption data without direct access, a blockchain-based federated learning method is proposed… More >

  • Open Access

    ARTICLE

    Research on Short-Term Load Forecasting of Distribution Stations Based on the Clustering Improvement Fuzzy Time Series Algorithm

    Jipeng Gu1, Weijie Zhang1, Youbing Zhang1,*, Binjie Wang1, Wei Lou2, Mingkang Ye3, Linhai Wang3, Tao Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2221-2236, 2023, DOI:10.32604/cmes.2023.025396

    Abstract An improved fuzzy time series algorithm based on clustering is designed in this paper. The algorithm is successfully applied to short-term load forecasting in the distribution stations. Firstly, the K-means clustering method is used to cluster the data, and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division. On this basis, the data is fuzzed to form a fuzzy time series. Secondly, a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load, which is used to predict the short-term trend change of load in the… More >

Displaying 21-30 on page 3 of 153. Per Page