Jiachang Liu^1,*, Zhengwei Huang², Junfeng Xiang¹, Lu Liu¹, Manlin Hu¹

Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >

Ramiz Gorkem Birdal^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4015-4028, 2024, DOI:10.32604/cmc.2024.048324 - 26 March 2024

Abstract Maintaining a steady power supply requires accurate forecasting of solar irradiance, since clean energy resources do not provide steady power. The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network (CNN), but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions. This paper proposes a hybrid approach based on deep learning, expanding the feature set by adding new air pollution concentrations, and ranking these features to select and reduce their size to improve efficiency. In… More >

Bobbinpreet¹, Sultan Aljahdali^2,*, Tripti Sharma¹, Bhawna Goyal¹, Ayush Dogra³, Shubham Mahajan⁴, Amit Kant Pandit⁴

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4771-4787, 2022, DOI:10.32604/cmc.2022.023150 - 21 April 2022

Abstract In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection… More >

K. Poongodi^1,*, A. Sabari²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 939-953, 2022, DOI:10.32604/iasc.2022.023038 - 08 February 2022

Abstract The microarray gene expression data has a large number of genes with different expression levels. Analyzing and classifying datasets with entire gene space is quite difficult because there are only a few genes that are informative. The identification of bio-marker genes is significant because it improves the diagnosis of cancer disease and personalized medicine is suggested accordingly. Initially, the parallelized minimum redundancy and maximum relevance ensemble (mRMRe) is employed to select top m informative genes. The selected genes are then fed into the Genetic Algorithm (GA) that selects the optimal set of genes heuristically, which More >