International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 09 | Sep 2025
p-ISSN: 2395-0072
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Hybrid CNN-SVM Regression Model for Accurate Energy Consumption Prediction in Smart Energy Systems Kartikay1, Dr. Geena sharma2, Er.Mahender pal3 1,2,3Electrical Engineering 1,2,3Baddi university of Emerging Science and Technology , Baddi Himachal Pradesh ---------------------------------------------------------------------------***--------------------------------------------------------------------------consumption forecasting methods rely on statistical and Abstract
econometric models, which may lack precision when dealing with large, complex datasets. Machine Learning (ML), a subset of Artificial Intelligence (AI), offers advanced predictive capabilities that improve energy consumption forecasting accuracy. The integration of ML in energy consumption prediction enables real-time analysis, adaptive learning, and robust pattern recognition, ensuring efficient energy management and sustainability.
The rapid increase in global energy demand, driven by urbanization and industrialization, highlights the need for accurate forecasting methods to ensure efficient energy management, cost reduction, and environmental sustainability. Traditional statistical and econometric models often fail to capture the complex, nonlinear dependencies in energy consumption patterns. In this study, advanced machine learning (ML) techniques are explored for energy consumption prediction, leveraging their ability to model nonlinearities, adapt to dynamic conditions, and utilize large datasets. A hybrid CNN-SVR approach is proposed, where Convolutional Neural Networks (CNN) extract meaningful spatial-temporal features from energy data, and Support Vector Regression (SVR) performs accurate forecasting. The model is evaluated alongside conventional methods such as Linear Regression, SVM, Random Forest, and Boosted Trees using performance metrics including MAE, MSE, RMSE, R², and MAPE. Results show that Boosted Trees outperformed others with the lowest error rates and high predictive accuracy, while the CNN-SVR model achieved the highest R² value, indicating strong generalization. Linear Regression also provided reliable results for linear dependencies but struggled with nonlinear patterns. These findings emphasize the potential of hybrid and ensemble ML models in enhancing predictive accuracy, offering robust solutions for sustainable energy planning, smart grid management, and demand-side optimization.
Moreover, increasing concerns regarding energy efficiency and sustainability have made accurate forecasting a crucial aspect of modern power systems. Traditional forecasting models often struggle to adapt to the non-linear nature of energy consumption patterns influenced by various factors such as weather conditions, economic activities, and behavioral aspects of consumers. ML-based methods, by contrast, offer dynamic, data-driven solutions that can continually improve in accuracy as more data is collected. The use of big data and cloud computing further enhances the potential of ML in energy forecasting, making it a promising field of study for researchers and energy professionals. This paper explores the role of machine learning in energy consumption prediction, highlighting its applications, advantages, and the latest advancements in the field.Global economic development is greatly accelerated by Energy Efficiency (EE) and Energy Conservation (EC) codes, standards, policies, and regulations, which increases demand for energy and addresses climate change. Because of its harmful consequences on the environment, energy waste is becoming a bigger problem. Therefore, at the level of residential buildings, legislators or decision-makers ought to be particularly aware of the needs of EE and EC [1]. More than 2.5 billion populations of global urban areas are increasing by 2050, so there is an urgent need for development and modernized existing city structures, so-called "smart cities.” So, we have to meet the goals of the Paris Agreement on de-carbonization by 2050. The building sector's present energy performance is poor and needs to be upgraded. The building sector accounts for over 40% of
Keywords: energy forecasting, machine learning, CNNSVR, boosted trees, smart grids
I INTRODUCTION The global energy demand is rising exponentially due to rapid industrialization, urbanization, and population growth. Effective energy management and forecasting are essential to optimize energy use, reduce costs, and minimize environmental impact. Traditional energy
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