Comparative analysis of Direct and Indirect Model Reference Adaptive Control by Extended Kalman Filter
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Smart Wires and Modular FACTS Controllers for Smart Grid Applications: A Review
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Integrated Renewable Energy Management System for Reduced Hydrogen Consumption using Fuel Cell
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Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure
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Artificial Intelligence based Business Process Automation for Enhanced Knowledge Management
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Unmanned Aerial Vehicle with Thermal Imaging for Automating Water Status in Vineyard
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Design of Effective Smart Communication System for Impaired People
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Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
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Prediction of Energy Consumption by Ships at the port using Deep Learning
Volume-3 | Issue-2
A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
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Power Transfer Capability Recognition in Deregulated System under Line Outage Condition Using Power World Simulator
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Transformer Oil Diagnostic Tests Analysis using Statistical Correlation Technique
Volume-4 | Issue-3
Design of Inverter Voltage Mode Controller by Backstepping Technique for Nonlinear Power System Model
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Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
Volume-3 | Issue-3
Performance Analysis of Multiple Pico Hydro Power Generation
Volume-2 | Issue-2
Energy Efficient Data Mining Approach for Estimating the Diabetes
Volume-3 | Issue-2
Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure
Volume-3 | Issue-3
Prediction of Energy Consumption by Ships at the port using Deep Learning
Volume-3 | Issue-2
A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
Volume-3 | Issue-4
Unmanned Aerial Vehicle with Thermal Imaging for Automating Water Status in Vineyard
Volume-3 | Issue-2
Volume - 5 | Issue - 2 | june 2023
Published
06 June, 2023
Electricity demand forecasting is an essential task in the energy industry, enabling utilities and energy suppliers to optimize the generation, transmission, and distribution of electricity. In recent years, deep learning techniques such as Long Short -Term Memory (LSTM) neural networks have shown great potential in improving the accuracy and efficiency of time-series forecasting tasks, including electricity demand forecasting. This research proposes an LSTM-based neural network architecture for short-term electricity demand forecasting. The proposed model is evaluated on real-world electricity demand data, and the results demonstrate its effectiveness in predicting future demand patterns. The model's performance is evaluated using the Mean Squared Error loss function and the Root Mean Squared Error metric. The proposed model shows promising results compared to traditional time-series forecasting models. The results suggest that LSTM-based neural networks can be a powerful tool for electricity demand forecasting, providing more accurate and efficient forecasting models that can help improve energy system planning and decision making.
KeywordsDemand forecasting LSTMs Deep Learning Root Mean Square Error
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