International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 04 | Apr 2026
p-ISSN: 2395-0072
www.irjet.net
Research on to Develop an AI Based Model for Electricity Demand Projection Including Peak Demand Projection for Delhi Power System Prof. S.O. Sahu¹, Madhura R. Hutke², Samiksha M. Lahane³, Sakshi R. Uke´, Ankita V. Rathodµ 1Professor, B.E. Computer Science and Engineering, Sipna College of Engineering and Technology, Amravati,
Maharashtra, India, sosahu@sipnaengg.ac.in ²³´µGraduate Student, B.E. Computer Science and Engineering, Sipna College of Engineering and Technology, Amravati, Maharashtra, India, ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The increasing complexity of electricity
efficient resource utilization, and uninterrupted power supply.
consumption patterns in metropolitan cities such as Delhi has made accurate demand and peak load forecasting a critical requirement for efficient power system planning and operation. Rapid urbanization, climate variability, and rising use of energy-intensive appliances contribute to significant fluctuations in electricity demand, particularly during peak periods. Traditional forecasting techniques often fail to capture the non-linear relationships between demand, weather conditions, and temporal factors. This paper presents the development of an AI-based electricity demand projection system designed to forecast both overall load and peak demand for the Delhi power system. Historical electricity consumption and related datasets are analyzed and preprocessed using data science techniques, including feature engineering and normalization. Machine learning models such as Random Forest, Support Vector Machine, and XGBoost are implemented to model complex demand patterns, with SMOTE applied where required to handle data imbalance during peak demand scenarios. Model performance is evaluated using standard error metrics to identify the most accurate forecasting approach. A Python Flask-based web application is developed to provide interactive visualization of demand forecasts and power analysis results. The proposed system demonstrates improved forecasting accuracy and reliability, supporting better operational planning, peak load management, and sustainable energy management for urban power systems.
Traditional electricity demand forecasting methods primarily rely on historical averages and linear statistical models. While these approaches have been widely used, they often struggle to capture the complex interactions between multiple influencing factors such as temperature, humidity, holidays, and economic activity. As a result, conventional models frequently produce inaccurate predictions during peak demand periods, increasing the risk of grid stress, power outages, and costly emergency power procurement. The growing availability of large-scale historical load data, weather information, and advancements in data science have enabled the adoption of Artificial Intelligence (AI) and Machine Learning (ML) techniques for electricity demand forecasting. AI-based models such as Random Forest, Support Vector Machines, and XGBoost are capable of learning complex, non-linear relationships within data, making them more effective for demand and peak load prediction compared to traditional approaches. These models can adapt to changing consumption patterns and provide improved forecasting accuracy under varying operating conditions. In this context, this paper aims to develop an AI-based electricity demand projection system for the Delhi power system, with a specific focus on peak demand forecasting. The proposed system utilizes historical electricity demand and related datasets to train and evaluate multiple machine learning models. Additionally, a Python Flask-based web interface is developed to visualize demand forecasts and perform power analysis, enabling data-driven decision-making for power system planning and management. The outcomes of this paper contribute toward improving grid reliability, reducing operational costs, and supporting sustainable energy management in large urban power systems.
Key words: Electricity Demand Projection, Random Forest, SVM, SMOTE, XGBoost.
1. INTRODUCTION Electricity demand forecasting plays a crucial role in the planning, operation, and reliability of modern power systems. In rapidly growing metropolitan cities such as Delhi, the continuous increase in population, urban infrastructure, and commercial activity has led to a significant rise in electricity consumption. Seasonal variations, extreme weather conditions, and changing consumer behavior further contribute to highly dynamic and non-linear demand patterns. Accurate forecasting of electricity demand, particularly during peak load periods, is therefore essential to ensure grid stability,
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2. LITERATURE REVIEW 2.1. Kavitha Juliet, et al. paper presents an IEEE-style, AI-based forecasting framework that combines classical machine learning methods with advanced deep learning architectures such as Long Short-
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