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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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Revolutionizing Energy Predictions: Unleashing The Potential of Hybrid Machine Learning for Accurate Household Appliance Consumption and Peak Demand Forecasting
Authors
Sarika. S,Manjusha. V.A,Sneha Narayanan
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Keywords
Machine learning, Data filtering, Electrical appliance consumption, Peak demand, Smart meters
Abstract
Accurate forecasting of electrical appliance usage and peak demand is crucial for effective planning, maintenance, and the development of automation in electrical power systems. Discrepancies between actual appliance consumption and energy demand may arise from various factors, such as losses in lines and appliances, as well as mismanagement of energy demand. A groundbreaking approach for forecasting household electric appliance consumption and peak demand through a hybrid machine learning (ML) framework is presented here. To address these variations, a thorough examination of smart meter data is essential to pinpoint the key attributes and primary causes of differences between electrical appliance consumption and customers' peak demand. Understanding these intricacies is vital for optimizing power system operations and implementing strategies to enhance efficiency and reliability. The study employs a comprehensive dataset spanning multiple households, ensuring the generalizability of the proposed methodology. Results demonstrate superior predictive capabilities compared to traditional models, offering significant advancements in energy demand forecasting precision. This hybrid approach not only captures nuanced relationships within the data but also adapts dynamically to changing environmental and behavioral factors. As the energy landscape evolves, our innovative methodology stands poised to revolutionize how we understand and anticipate household electricity consumption, providing valuable insights for policymakers, utility providers, and consumers alike.
IJCRT's Publication Details
Unique Identification Number - IJCRT2311375
Paper ID - 246256
Author type - Indian Author
Page Number(s) - d212-d217
Pubished in - Volume 11 | Issue 11 | November 2023
DOI (Digital Object Identifier) -
No Of Downloads - 60
Author Country - India, 689501, Pathanamthitta, Pathanamthitta, 689501, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Sarika. S,Manjusha. V.A,Sneha Narayanan, "Revolutionizing Energy Predictions: Unleashing The Potential of Hybrid Machine Learning for Accurate Household Appliance Consumption and Peak Demand Forecasting", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 11, pp.d212-d217, November 2023, Available at :http://www.ijcrt.org/papers/IJCRT2311375.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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