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Intelligent Residential Demand Management via Bi-LSTM Forecasting and MPC-Based Optimization
Students & Supervisors
Student Authors
Raif Tanjim
Bachelor of Science in Electrical & Electronic Engineering, FE
Tanvir Ahmed
Bachelor of Science in Electrical & Electronic Engineering, FE
Abdullah Al Mujahid
Bachelor of Science in Electrical & Electronic Engineering, FE
Supervisors
Abu Shufian
Lecturer, Faculty, FE
Abstract
This study proposes a Bi-LSTM-enhanced MPC framework for residential DSM, integrating deep learning-based forecasting with optimization-driven control. Results demonstrate superior performance over conventional DSM: 25.13% peak reduction (11.48 kWh), 54.93% lower energy losses (46.17 kWh, 0.67% of total consumption), and 317.09 kWh shifted to off-peak periods without compromising user comfort. Sub-meter analysis identifies Water & AC as the primary DSM contributor, while robust seasonal performance confirms scalability and practical relevance.
Keywords
DSM
Bi-LSTM
MPC
Load Forecasting
Peak Load Reduction
Smart Grid Integration.
Publication Details
- Type of Publication:
- Conference Name: IEEE Region 10 TENSYMP 2026
- Date of Conference: 29/06/2026 - 29/06/2026
- Venue: IEEE Malaysia Section, Penang, Malaysia
- Organizer: IEEE Malaysia Section, Penang, Malaysia