Nataliya Shakhovska^1,2,*, Mykola Medykovskyi¹, Oleksandr Gurbych^1,3, Mykhailo Mamchur^1,3, Mykhailo Melnyk¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3147-3163, 2024, DOI:10.32604/cmc.2024.056542 - 18 November 2024

Abstract The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability, reliability, and economic benefits. This study explores advanced machine learning (ML) and deep learning (DL) techniques for predicting solar energy generation, emphasizing the significant impact of meteorological data. A comprehensive dataset, encompassing detailed weather conditions and solar energy metrics, was collected and preprocessed to improve model accuracy. Various models were developed and trained with different preprocessing stages. Finally, three datasets were prepared. A novel hour-based prediction wrapper was introduced, utilizing external sunrise and sunset data to restrict… More >

Abdennasser Dahmani¹, Yamina Ammi², Nadjem Bailek^3,4,*, Alban Kuriqi^5,6, Nadhir Al-Ansari^7,*, Salah Hanini², Ilhami Colak⁸, Laith Abualigah^{9,10,11,12,13,14}, El-Sayed M. El-kenawy¹⁵

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2579-2594, 2023, DOI:10.32604/cmc.2023.040625 - 29 November 2023

Abstract Increasing global energy consumption has become an urgent problem as natural energy sources such as oil, gas, and uranium are rapidly running out. Research into renewable energy sources such as solar energy is being pursued to counter this. Solar energy is one of the most promising renewable energy sources, as it has the potential to meet the world’s energy needs indefinitely. This study aims to develop and evaluate artificial intelligence (AI) models for predicting hourly global irradiation. The hyperparameters were optimized using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton training algorithm and STATISTICA software. Data from two stations… More >

Abdelwahed Motwakel^1,*, Marwa Obayya², Nadhem Nemri³, Khaled Tarmissi⁴, Heba Mohsen⁵, Mohammed Rizwanulla⁶, Ishfaq Yaseen⁶, Abu Sarwar Zamani⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1267-1281, 2023, DOI:10.32604/csse.2023.037735 - 26 May 2023

Abstract Recently, renewable energy (RE) has become popular due to its benefits, such as being inexpensive, low-carbon, ecologically friendly, steady, and reliable. The RE sources are gradually combined with non-renewable energy (NRE) sources into electric grids to satisfy energy demands. Since energy utilization is highly related to national energy policy, energy prediction using artificial intelligence (AI) and deep learning (DL) based models can be employed for energy prediction on RE and NRE power resources. Predicting energy consumption of RE and NRE sources using effective models becomes necessary. With this motivation, this study presents a new multimodal… More >

Cherifa Nakkach^*, Amira Zrelli, Tahar Ezzedine

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 545-560, 2023, DOI:10.32604/iasc.2023.036385 - 29 April 2023

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 More >

S. Balaji^*, S. Karthik

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 443-459, 2023, DOI:10.32604/cmc.2023.035275 - 06 February 2023

Abstract The Internet of Things (IoT) technology has been developed for directing and maintaining the atmosphere in smart buildings in real time. In order to optimise the power generation sector and schedule routine maintenance, it is crucial to predict future energy demand. Electricity demand forecasting is difficult because of the complexity of the available demand patterns. Establishing a perfect prediction of energy consumption at the building’s level is vital and significant to efficiently managing the consumed energy by utilising a strong predictive model. Low forecast accuracy is just one of the reasons why energy consumption and… More >

Xiaokan Wang^1,2,*, Qiong Wang¹, Liang Shuang³, Chao Chen⁴

Computer Systems Science and Engineering, Vol.39, No.2, pp. 237-250, 2021, DOI:10.32604/csse.2021.015945 - 20 July 2021

Abstract Electric vehicles such as trains must match their electric power supply and demand, such as by using a composite energy storage system composed of lithium batteries and supercapacitors. In this paper, a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train. The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain. Real-time online control of power allocation in the composite energy storage system can be achieved. Using standard More >

Mohammad Aldossary^*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3531-3562, 2021, DOI:10.32604/cmc.2021.017477 - 06 May 2021

Abstract With the striking rise in penetration of Cloud Computing, energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’ infrastructures. Subsequently, recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources, where the energy consumption and the operational costs are minimized. However, to make better cost decisions in these strategies, the performance and energy awareness should be supported at both Physical Machine (PM) and Virtual Machine (VM) levels. Therefore, in this paper, a novel hybrid approach is proposed, which… More >

Mohammad Aldossary^*

Computer Systems Science and Engineering, Vol.36, No.2, pp. 353-368, 2021, DOI:10.32604/csse.2021.014974 - 05 January 2021

Abstract With the expansion of cloud computing, optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important, since it directly affects providers’ revenue and customers’ payment. Thus, providing prediction information of the cloud services can be very beneficial for the service providers, as they need to carefully predict their business growths and efficiently manage their resources. To optimize the use of cloud services, predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs. However, such mechanisms need to be provided with energy awareness not only at the level of More >