K. Sukanya^*, P. Vijayakumar

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 971-982, 2023, DOI:10.32604/iasc.2023.028047

Abstract Frequency deviation has to be controlled in power generation units when there are fluctuations in system frequency. With several renewable energy sources, wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature. Whenever there is a mismatch between generation and demand, the frequency deviation may arise from the actual frequency 50 Hz (in India). To mitigate the frequency deviation issue, it is necessary to develop an effective technique for better frequency control in wind energy systems. In this work, heuristic Fuzzy Logic Based Controller (FLC) is developed for providing… More >

Ziad M. Ali^1,2,*, Ahmed M. Galal^1,3, Salem Alkhalaf⁴, Imran Khan⁵

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 755-767, 2023, DOI:10.32604/iasc.2023.027568

Abstract The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids and energy management on the load side. Microgrid can effectively solve this problem by using its regulation and flexibility, and is considered to be an ideal platform. The traditional method of computing total transfer capability is difficult due to the central integration of wind farms. As a result, the differential evolution extreme learning machine is offered as a data mining approach for extracting operating rules for the total transfer capability of tie-lines in wind-integrated power systems. K-medoids clustering under the two-dimensional… More >

M. Mohan^1,*, P. C. Kishore Raja², P. Velmurugan³, A. Kulothungan⁴

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1151-1163, 2023, DOI:10.32604/iasc.2023.026255

Abstract Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss. The proposed model uses a real time dataset of fifteen Stocks as input into the system and based on the data, predicts or forecast future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not; the forecasting… More >

P. Renukadevi^1,*, A. Rajiv Kannan²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 629-645, 2023, DOI:10.32604/csse.2023.024217

Abstract About 170 nations have been affected by the COvid VIrus Disease-19 (COVID-19) epidemic. On governing bodies across the globe, a lot of stress is created by COVID-19 as there is a continuous rise in patient count testing positive, and they feel challenging to tackle this situation. Most researchers concentrate on COVID-19 data analysis using the machine learning paradigm in these situations. In the previous works, Long Short-Term Memory (LSTM) was used to predict future COVID-19 cases. According to LSTM network data, the outbreak is expected to finish by June 2020. However, there is a chance of an over-fitting problem in… More >

Muhammad Hamza Azam¹, Mohd Hilmi Hasan^1,*, Azlinda A Malik², Saima Hassan³, Said Jadid Abdulkadir¹

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1799-1815, 2022, DOI:10.32604/cmc.2022.028292

Abstract Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future prices. Electricity price forecasting have been a critical input to energy corporations’ strategic decision-making systems over the last 15 years. Many strategies have been utilized for price forecasting in the past, however Artificial Intelligence Techniques (Fuzzy Logic and ANN) have proven to be more efficient than traditional techniques (Regression and Time Series). Fuzzy logic is an approach that uses membership functions (MF) and fuzzy inference model to forecast future electricity prices. Fuzzy c-means (FCM) is one of the popular… More >

Lina Wang^1,2,*, Xilin Deng¹, Peng Ge¹, Changming Dong^2,3, Brandon J. Bethel³, Leqing Yang¹, Jinyue Xia⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2151-2168, 2022, DOI:10.32604/cmc.2022.027415

Abstract Though numerical wave models have been applied widely to significant wave height prediction, they consume massive computing memory and their accuracy needs to be further improved. In this paper, a two-dimensional (2D) significant wave height (SWH) prediction model is established for the South and East China Seas. The proposed model is trained by Wave Watch III (WW3) reanalysis data based on a convolutional neural network, the bi-directional long short-term memory and the attention mechanism (CNN-BiLSTM-Attention). It adopts the convolutional neural network to extract spatial features of original wave height to reduce the redundant information input into the BiLSTM network. Meanwhile,… More >

Al-Batool Al-Ghamdi^1,*, Souad Kamel², Mashael Khayyat³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1365-1383, 2022, DOI:10.32604/cmc.2022.026246

Abstract Water is a vital resource. It supports a multitude of industries, civilizations, and agriculture. However, climatic conditions impact water availability, particularly in desert areas where the temperature is high, and rain is scarce. Therefore, it is crucial to forecast water demand to provide it to sectors either on regular or emergency days. The study aims to develop an accurate model to forecast daily water demand under the impact of climatic conditions. This forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the future. Focusing on the… More >

Charnon Chupong, Boonyang Plangklang^*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5415-5427, 2022, DOI:10.32604/cmc.2022.028416

Abstract This article presents hourly load forecasting by using an incremental learning model called Online Sequential Extreme Learning Machine (OS-ELM), which can learn and adapt automatically according to new arrival input. However, the use of OS-ELM requires a sufficient amount of initial training sample data, which makes OS-ELM inoperable if sufficiently accurate sample data cannot be obtained. To solve this problem, a synthesis of the initial training sample is proposed. The synthesis of the initial sample is achieved by taking the first data received at the start of working and adding random noises to that data to create new and sufficient… More >

Elias Hossain¹, Abdulwahab Alazeb^2,*, Naif Al Mudawi², Sultan Almakdi², Mohammed Alshehri², M. Gazi Golam Faruque³, Wahidur Rahman³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4945-4966, 2022, DOI:10.32604/cmc.2022.027058

Abstract Depression is a crippling affliction and affects millions of individuals around the world. In general, the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts, which results in lower costs and improved patient outcomes. However, this strategy can necessitate a lot of buy-in from a large number of people, as well as additional training and logistical considerations. Thus, utilizing the machine learning algorithms, patients with depression based on information generally present in a medical file were analyzed and predicted. The methodology of this proposed study is… More >

Muhammad Irfan¹, Ali Raza^2,*, Faisal Althobiani³, Nasir Ayub^4,5, Muhammad Idrees⁶, Zain Ali⁷, Kashif Rizwan⁴, Abdullah Saeed Alwadie¹, Saleh Mohammed Ghonaim³, Hesham Abdushkour³, Saifur Rahman¹, Omar Alshorman¹, Samar Alqhtani⁸

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4249-4265, 2022, DOI:10.32604/cmc.2022.025863

Abstract In the Smart Grid (SG) residential environment, consumers change their power consumption routine according to the price and incentives announced by the utility, which causes the prices to deviate from the initial pattern. Thereby, electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability. Due to the massive amount of data, big data analytics for forecasting becomes a hot topic in the SG domain. In this paper, the changing and non-linearity of consumer consumption pattern complex data is taken as input. To minimize the computational cost and complexity of the data, the… More >