Bingbing Chen^*, Zhengyi Zhu, Xuyan Wang, Can Zhang

Energy Engineering, Vol.118, No.5, pp. 1499-1514, 2021, DOI:10.32604/EE.2021.015145

Abstract To solve the medium and long term power load forecasting problem, the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward. This model is divided into two stages which are forecasting model selection and weighted combination forecasting. Based on Markov chain conversion and cloud model, the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting. For the weighted combination forecasting, a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model. The percentage error and mean absolute percentage error of… More >

Zhenzhou Wang¹, Yinuo Ma¹, Pingping Yu^1,*, Ning Cao², Heiner Dintera³

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3431-3446, 2021, DOI:10.32604/cmc.2021.017729

Abstract Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes. The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience, which have problems such as inaccurate recognition time, time-consuming and energy sapping. Therefore, this paper proposes a neural network-based method for predicting the flowering phase of pear tree. Firstly, based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019, three principal components (the temperature factor, weather factor, and humidity factor) with high correlation coefficient with the flowering phase… More >

Jaeuk Moon, Seungwon Jung, Sungwoo Park, Eenjun Hwang^*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2945-2959, 2021, DOI:10.32604/cmc.2021.017435

Abstract One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country’s long-term influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection… More >

Mei Sun¹, Qingtao Li², Peiguang Lin^2,*

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 369-378, 2021, DOI:10.32604/iasc.2021.014962

Abstract Stocks are the key components of most investment portfolios. The accurate forecasting of stock prices can help investors and investment brokerage firms make profits or reduce losses. However, stock forecasting is complex because of the intrinsic features of stock data, such as nonlinearity, long-term dependency, and volatility. Moreover, stock prices are affected by multiple factors. Various studies in this field have proposed ways to improve prediction accuracy. However, not all of the proposed features are valid, and there is often noise in the features—such as political, economic, and legal factors—which can lead to poor prediction results. To overcome such limitations,… More >

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

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 method (henceforth referred to as… 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

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 load duration based on time-of-use.… 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

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 use the Autoregressive Integrated Moving… 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

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, where the response variables were… 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

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 than 2.5 micrometers, the inhalation… 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

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 researching on the potential vaccine… More >