Building an Intelligent Energy Management System for Enhancing Energy Consumption in Smart Houses
DOI:
https://doi.org/10.31642/JoKMC/2018/130108Keywords:
Intelligent Energy Management System, Energy Consumption, Smart HousesAbstract
This trend of increasing residential energy consumption accounts for a significant and growing fraction of total global electricity demand — it also presents a bad economic incentive while being a burden for grid stability and sustainableisation. Existing smart home energy management systems typically do not strike an adequate trade-off between the three main conflicting objectives, namely, operational cost minimization, occupant thermal comfort satisfaction, and renewable energy usage maximization. To fill this gap, this paper presents a new Intelligent Energy Management System (IEMS) based on a Mixed-Integer Linear Programming (MILP) framework. It combines all the large and complex real-world inputs — hourly solar photovoltaic (PV) generation forecasts, dynamic time-of-use (TOU) electricity pricing, user-defined appliance preference windows, lithium-ion battery dynamics, and a linearized HVAC thermal model — into one single, tractable optimization problem. Calculated for a typical winter day in Helsinki, data from Climate-Data. org and Oomi. To summarize, the proposed IEMS can reduce daily electricity expenses by 73.1% (from €1.04 to €0.2805) and 68% of grid price, while simultaneously maintaining indoor temperature strictly between comfort band of 20–24°C and without violating user scheduling preferences. Our results show that the MILP approach provides a mathematically tractable, transparent and reproducible solution which outperforms rule-based heuristics and uncoordinated operation. This work provides such a generalizable and implementable framework for sustainable and demand-centric home energy management in smart grids
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