Page 211 - 2024-Vol20-Issue2

Received: 10 August 2023 | Revised: 18 September 2023 | Accepted: 9 December 2023

DOI: 10.37917/ijeee.20.2.17 Vol. 20 | Issue 2 | December 2024

Open Access

Iraqi Journal for Electrical and Electronic Engineering

Original Article

Comparative Long-Term Electricity Forecasting Analysis:

A Case Study of Load Dispatch Centres in India

Saikat Gochhait*1, Deepak K. Sharma1, Mrinal Bachute2
1Symbiosis Institute of Digital and Telecom Management, Symbiosis International Deemed University, Pune, India

2Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India

Correspondance
*Saikat Gochhait
Symbiosis Institute of Digital and Telecom Management,
Symbiosis International Deemed University, Pune, India
Email: saikat.gochhait@sidtm.edu.in

Abstract
Accurate long-term load forecasting (LTLF) is crucial for smart grid operations, but existing CNN-based methods face
challenges in extracting essential featuresfrom electricity load data, resulting in diminished forecasting performance.
To overcome this limitation, we propose a novel ensemble model that integratesa feature extraction module, densely
connected residual block (DCRB), longshort-term memory layer (LSTM), and ensemble thinking. The feature extraction
module captures the randomness and trends in climate data, enhancing the accuracy of load data analysis. Leveraging
the DCRB, our model demonstrates superior performance by extracting features from multi-scale input data, surpassing
conventional CNN-based models. We evaluate our model using hourly load data from Odisha and day-wise data from
Delhi, and the experimental results exhibit low root mean square error (RMSE) values of 0.952 and 0.864 for Odisha
and Delhi, respectively. This research contributes to a comparative long-term electricity forecasting analysis, showcasing
the efficiency of our proposed model in power system management. Moreover, the model holds the potential to sup-port
decisionmaking processes, making it a valuable tool for stakeholders in the electricity sector.

Keywords
Long-term load forecasting, Ensemble model, Feature extraction, Multi-scale input, Densely connected residual
block, Bidirectional long short-termmemory, smart grid, power system management.

I. INTRODUCTION energy sources and energy storage systems.
To provide a thorough understanding of the subject, a com-
The demand for electricity continues to rise globally, driven by prehensive literature review has been conducted. The review
population growth, urbanization, and industrialization. Meet- incorporates key studies and research papersthat contribute
ing this growing demand while ensuring a reliable and sus- to the field of electricity forecasting, renewable energy inte-
tainable energy supply is a critical challenge for policymakers, gration, and energy storage optimization. The selected papers
energy planners, and researchers. Renewable energy sources cover various aspects such as optimization algorithms, system
and energy storage systems haveemerged as promising solu- design, sizing methodologies, economic operation, machine
tions to address this challenge by providing clean, reliable, and learning techniques, and intelligent control. One prominent
flexible electricity generation and management. The objective study by Kharrich et al. [1] introduces an improved arithmetic
of this research paper entitled is to conduct a comprehen- optimization algorithm for the design of a micro grid with an
sive analysisof long-term electricity forecasting in two Indian energy storage system in El Kharga Oasis, Egypt. Wong et
states, Orissa and Delhi. This analysisaims to assess the ef- al. [2] present a review of the optimal placement, sizing, and
fectiveness and accuracy of different forecasting methods and control of energy storage systems in the distribution network,
models, particularly focusing on the integration of renewable

This is an open-access article under the terms of the Creative Commons Attribution License,
which permits use, distribution, and reproduction in any medium, provided the original work is properly cited.
©2024 The Authors.
Published by Iraqi Journal for Electrical and Electronic Engineering | College of Engineering, University of Basrah.

https://doi.org/10.37917/ijeee.20.2.17 |https://www.ijeee.edu.iq 207