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Article type: Research Article
Authors: Vaikunta Pai, T.a; * | Singh, Manmohanb | Shaik, Nazeerc | Ashokkumar, C.d | Anuradha, D.e | Gangopadhyay, Amitf | Rao, Goda Srinivasag | Reddy, T.Sunilkumarh | Nagaraju, D.i
Affiliations: [a] Department of Information Science and Engineering, NMAM Institute of Technology-Affiliated (NITTE (Deemed to be University), Nitte, Karnataka, India | [b] Department of Computer Science and Engineering, IES College of Technology Bhopal, Madhya Pradesh, India | [c] Department of CSE, Srinivasa Ramanujan Institute of Technology (Autonomous), Anantapur, Andhra Pradesh, India | [d] Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India | [e] Department of Computer Science and Business Systems, Panimalar Engineering College, Chennai, India | [f] Department of Electronics and Communication Engineering, Mohan Babu University, Erstwhile Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India | [g] Department of CSE, KL University, Guntur, Andhra Pradesh, India | [h] Department of Computer Science and Engineering, Sri Venkatasa Perumal College of Engineering and Technology, Puttur, India | [i] Department of CSE, Sri Venkatesa Perumal College of Engineering and Technology, Puttur, Andhra Pradesh, India
Correspondence: [*] Corresponding author. T. Vaikunta Pai, Department of Information Science and Engineering, NMAM Institute of Technology-Affiliated (NITTE (Deemed to be University), Nitte-574110, Karnataka, India. E-mail: [email protected].
Abstract: 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
Keywords: Auto-regressive, distributed lag, energy consumption, forecast, triangular coefficient
DOI: 10.3233/JIFS-240729
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
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