分布滞后
滞后
计算机科学
模糊逻辑
自回归模型
时滞
能量(信号处理)
能源需求
需求预测
计量经济学
人工智能
机器学习
统计
运筹学
数学
经济
环境经济学
计算机网络
作者
T Vaikunta Pai,Manmohan Singh,Nazeer Shaik,C. Ashokkumar,D. Anuradha,Amit Gangopadhyay,Goda Srinivasa Rao,T. Jaya Venkata Rama Reddy,Dega Nagaraju
出处
期刊:Journal of Intelligent and Fuzzy Systems
[IOS Press]
日期:2024-03-15
卷期号:: 1-12
摘要
As the demand for energy in India continues to surge, accurate forecasting becomes paramount for efficient resource allocation and sustainable development. This study proposes an innovative approach to forecasting Indian primary energy demand by integrating Artificial Intelligence (AI) techniques with Fuzzy Auto-regressive Distributed Lag (FADL) models. FADL models, incorporating fuzzy logic, allow for a nuanced representation of uncertainties and complexities within the energy demand dynamics. In this research, historical energy consumption data is analysed using FADL models with both symmetric and non-symmetric triangular coefficients, enhancing the model’s adaptability to the inherent uncertainties associated with energy forecasting. This study addresses the urgent need for enhanced energy planning models in the context of sustainable development. Our research aims to provide a comprehensive framework for predicting future Total Final Consumption (TFC) in alignment with the Indian National Energy Plan’s net-zero emissions target by 2035. Recognizing the limitations of current models, our research introduces a novel approach that integrates advanced algorithms and methodologies, offering a more flexible and realistic assessment of TFC trends. The primary objective of this study is to develop an improved energy planning model that surpasses existing projections by incorporating sophisticated algorithms. We aim to refine
科研通智能强力驱动
Strongly Powered by AbleSci AI