气候变化领域集成服务门户(CCPortal): Modeling influence of weather variables on energy consumption in an agricultural research institute in Ibadan, Nigeria

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DOI	10.3934/energy.2024012
	Modeling influence of weather variables on energy consumption in an agricultural research institute in Ibadan, Nigeria
	Abu Rahaman; Amakor, John; Kazeem, Rasaq; Olugasa, Temilola; Ajide, Olusegun; Idusuyi, Nosa; Jen, Tien -Chien; Akinlabi, Esther
发表日期	2024
ISSN	2333-8326
EISSN	2333-8334
起始页码	12
结束页码	1
卷号	12 期号:1
英文摘要	Climate change is having a significant impact on weather variables like temperature, humidity, precipitation, solar radiation, daylight duration, wind speed, etc. These weather variables are key indicators that affect electricity demand and consumption. Hence, understanding the significance of weather elements on energy needs and consumption is important to be able to adapt, strategize, and predict the effect of the changing climate on the required energy of an organization. This study aims to investigate the relationship between changing weather elements and electricity consumption, employing Multivariate Linear Regression (MLR), Support Vector Regressions (SVR), and Artificial Neural Network (ANN) models to predict the effect of weather changes on energy consumption. The following approaches were engaged for this study: Creating a catalog of weather elements and parameters of energy need or its consumption; analyzing and correlating electrical power consumption to weather factors; and developing prediction models-MLR, SVR, and ANN to predict the significance of the change in the variables of weather on the electrical energy consumption. Among the weather variables considered, temperature emerged as the most influential factor affecting electricity consumption, displaying the highest correlation. The monthly total pattern for electricity use for the case study area followed a similar pattern as the mean apparent temperature. Of the three models (MLR, SVR, and ANN) developed in this study, the ANN model yielded the best predictive performance, with Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of 2.733%, 1.292%, and 4.66%, respectively. Notably, the ANN model outperformed the other models (MLR and SVR) by more than 20% across the predictive performance metrics employed.
英文关键词	ANN; weather variable; MLR; energy consumption; SVR
语种	英语
WOS研究方向	Energy & Fuels
WOS类目	Energy & Fuels
WOS记录号	WOS:001155851900002
来源期刊	AIMS ENERGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/301047
作者单位	University of Ibadan; CGIAR; International Institute of Tropical Agriculture (IITA); University of Johannesburg; Northumbria University
推荐引用方式 GB/T 7714	Abu Rahaman,Amakor, John,Kazeem, Rasaq,et al. Modeling influence of weather variables on energy consumption in an agricultural research institute in Ibadan, Nigeria[J],2024,12(1).
APA	Abu Rahaman.,Amakor, John.,Kazeem, Rasaq.,Olugasa, Temilola.,Ajide, Olusegun.,...&Akinlabi, Esther.(2024).Modeling influence of weather variables on energy consumption in an agricultural research institute in Ibadan, Nigeria.AIMS ENERGY,12(1).
MLA	Abu Rahaman,et al."Modeling influence of weather variables on energy consumption in an agricultural research institute in Ibadan, Nigeria".AIMS ENERGY 12.1(2024).