Vin Cent Tai^1,*, Yong Chai Tan¹, Nor Faiza Abd Rahman¹, Hui Xin Che², Chee Ming Chia², Lip Huat Saw³, Mohd Fozi Ali⁴

Energy Engineering, Vol.118, No.3, pp. 715-725, 2021, DOI:10.32604/EE.2021.014865 - 22 March 2021

Abstract Electricity demand is also known as load in electric power system. This article presents a Long-Term Load Forecasting (LTLF) approach for Malaysia. An Artificial Neural Network (ANN) of 5-layer Multi-Layered Perceptron (MLP) structure has been designed and tested for this purpose. Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030. Pearson correlation was used to examine the input variables for model construction. The analysis indicates that Primary Energy Supply (PES), population, Gross Domestic Product (GDP) and temperature are strongly correlated. The forecast results by the proposed… More >

Jinseok Kim¹, Babar Shah², Ki-Il Kim^3,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 283-301, 2021, DOI:10.32604/cmc.2021.016042 - 22 March 2021

Abstract Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models. Especially, we need the adequate model to forecast the maximum load duration based on time-of-use, which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid. However, the existing single machine learning or deep learning forecasting cannot easily avoid overfitting. Moreover, a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum… More >

Xiaoli Qiang¹, Muhammad Aamir^2,*, Muhammad Naeem², Shaukat Ali³, Adnan Aslam⁴, Zehui Shao¹

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 841-856, 2021, DOI:10.32604/cmc.2021.012540 - 22 March 2021

Abstract COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world. Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce the number of new cases. In this study, we apply the decomposition and ensemble model to forecast COVID-19 confirmed cases, deaths, and recoveries in Pakistan for the upcoming month until the end of July. For the decomposition of data, the Ensemble Empirical Mode Decomposition (EEMD) technique is applied. EEMD decomposes the data into small components, called Intrinsic Mode Functions (IMFs). For individual IMFs modelling, we… More >

Samreen Naeem¹, Wali Khan Mashwani^2,*, Aqib Ali^1,3, M. Irfan Uddin⁴, Marwan Mahmoud⁵, Farrukh Jamal⁶, Christophe Chesneau⁷

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3451-3461, 2021, DOI:10.32604/cmc.2021.015872 - 01 March 2021

Abstract This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter (called tweets). A dataset of the exchange rates between the United States Dollar (USD) and the Pakistani Rupee (PKR) was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words. The dataset was collected in raw form, and was subjected to natural language processing by way of data preprocessing. Response variable labeling was then applied to the standardized dataset,… More >

R. Mangayarkarasi¹, C. Vanmathi^1,*, Mohammad Zubair Khan², Abdulfattah Noorwali³, Rachit Jain⁴, Priyansh Agarwal⁴

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3363-3380, 2021, DOI:10.32604/cmc.2021.014991 - 01 March 2021

Abstract Urbanization affects the quality of the air, which has drastically degraded in the past decades. Air quality level is determined by measures of several air pollutant concentrations. To create awareness among people, an automation system that forecasts the quality is needed. The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India. The overall air quality index (AQI) at any particular time is given as the maximum band for any pollutant. PM2.5 is a fine particulate matter of a size less… More >

Siddhant Banyal¹, Rinky Dwivedi², Koyel Datta Gupta², Deepak Kumar Sharma^3,*, Fadi Al-Turjman⁴, Leonardo Mostarda⁵

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1679-1696, 2021, DOI:10.32604/cmc.2021.014387 - 05 February 2021

Abstract The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and… More >

Debabrata Dansana¹, Raghvendra Kumar¹, Arupa Parida¹, Rohit Sharma², Janmejoy Das Adhikari¹, Hiep Van Le^3,*, Binh Thai Pham⁴, Krishna Kant Singh⁵, Biswajeet Pradhan^6,7,8,9

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1595-1612, 2021, DOI:10.32604/cmc.2021.012646 - 05 February 2021

Abstract The Coronavirus disease 2019 (COVID-19) outbreak was first discovered in Wuhan, China, and it has since spread to more than 200 countries. The World Health Organization proclaimed COVID-19 a public health emergency of international concern on January 30, 2020. Normally, a quickly spreading infection that could jeopardize the well-being of countless individuals requires prompt action to forestall the malady in a timely manner. COVID-19 is a major threat worldwide due to its ability to rapidly spread. No vaccines are yet available for COVID-19. The objective of this paper is to examine the worldwide COVID-19 pandemic,… More >

Guang Sun¹, Jingjing Lin^1,*, Chen Yang¹, Xiangyang Yin¹, Ziyu Li¹, Peng Guo^1,2, Junqi Sun³, Xiaoping Fan¹, Bin Pan¹

Computer Systems Science and Engineering, Vol.36, No.3, pp. 509-520, 2021, DOI:10.32604/csse.2021.014189 - 18 January 2021

Abstract Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage… More >

Ayesha Jabeen¹, Sitara Afzal¹, Muazzam Maqsood¹, Irfan Mehmood², Sadaf Yasmin¹, Muhammad Tabish Niaz³, Yunyoung Nam^4,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1191-1206, 2021, DOI:10.32604/cmc.2021.014598 - 12 January 2021

Abstract Stock market forecasting is an important research area, especially for better business decision making. Efficient stock predictions continue to be significant for business intelligence. Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns. However, major events’ news also contains significant information regarding market drivers. An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market. This research proposes an efficient model for stock market prediction. The current proposed study explores the More >

Quanjia Zuo¹, Fanyi Meng^1,*, Yang Bai²

Energy Engineering, Vol.118, No.2, pp. 303-322, 2021, DOI:10.32604/EE.2021.013239 - 23 December 2020

Abstract As one of the most important commodity futures, the price forecasting of natural gas futures is of great significance for hedging and risk aversion. This paper mainly focuses on natural gas futures pricing which considers seasonality fluctuations. In order to study this issue, we propose a modified approach called six-factor model, in which the influence of seasonal fluctuations are eliminated in every random factor. Using Monte Carlo method, we first assess and comparative analyze the fitting ability of three-factor model and six-factor model for the out of sample data. It is found that six-factor model More >