Mingliang Deng¹, Zhao Zhang^1,*, Hongyan Zhou², Xuebo Chen²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1189-1202, 2025, DOI:10.32604/cmc.2025.064509 - 09 June 2025

Abstract To fully explore the potential features contained in power load data, an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed. Firstly, a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data, accurately dividing subsets of data into different categories. Secondly, introducing convolutional block attention mechanism into the bidirectional gated recurrent unit (BiGRU) structure significantly enhances its ability to extract key features. On this basis, in order to make the model more accurately adapt to the dynamic changes… More >

Firas Abedi¹, Hayder M. A. Ghanimi², Mohammed A. M. Sadeeq³, Ahmed Alkhayyat^4,*, Zahraa H. Kareem⁵, Sarmad Nozad Mahmood⁶, Ali Hashim Abbas⁷, Ali S. Abosinnee⁸, Waleed Khaild Al-Azzawi⁹, Mustafa Musa Jaber^10,11, Mohammed Dauwed¹²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3359-3374, 2023, DOI:10.32604/cmc.2023.034221 - 31 March 2023

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 More >

Aishah Alrashidi, Ali Mustafa Qamar^*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 1973-1988, 2023, DOI:10.32604/csse.2023.024633 - 01 August 2022

Abstract Electrical load forecasting is very crucial for electrical power systems’ planning and operation. Both electrical buildings’ load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting. The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh, Saudi Arabia. The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017. These data are provided by King Abdullah City for… More >