An Efficient Artificial Bee Colony based Optimized Model for Load Prediction in IoT Enabled Smart Grid

In order to maintain a balance between demand and supply, the Internet of Things (IoT) enabled Smart Grid (SG) plays a critical role in establishing a Demand Response (DR) program. It is all about Demand Side Management (DSM) in SG's system. When IoT gadgets are programmed to turn on and off ac...

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Bibliographic Details
Published in2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS) pp. 129 - 136
Main Authors Manju, J, Manjula, R B, Dash, Ritesh
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.02.2023
Subjects
Artificial Bee Colony
Behavioral sciences
Internet of Things
Load forecasting
Load Prediction
Metaheuristics
Numerical models
Optimization
Planning
Power Consumption
Predictive models
S mart Grid
Smart grids
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Summary:In order to maintain a balance between demand and supply, the Internet of Things (IoT) enabled Smart Grid (SG) plays a critical role in establishing a Demand Response (DR) program. It is all about Demand Side Management (DSM) in SG's system. When IoT gadgets are programmed to turn on and off according to supply and demand, they become an essential part of the smart grid load prediction system and help to balance energy use. This research use Artificial Bee Colony (ABC) optimization model for load prediction in the smart grid environment. To effectively predict the load in the SG, an Efficient Artificial Bee Colony Optimized Model for Load Prediction in Smart Grid (EABCOM-LPSG) model is proposed in this research. The Artificial Bee Colony (ABC) algorithm is as warm-based meta-heuristic technique used for numerical problem optimization. It was inspired by honey bees' clever foraging behavior. The proposed method's two-step prediction system, specifically developed to improve forecasting precision as one of its major advantages. A major benefit of the suggested method is that it can statistically examine the effects of several major aspects, which is extremely useful when selecting attribute combinations and deploying on-board sensors for smart grids with large areas, diverse climates, and different social conventions. The proposed model when contrasted with traditional model exhibits better performance levels.
DOI:10.1109/ICAIS56108.2023.10073810