## 5 Optimization Control Strategies in Bems
### The Impact of Dual Optimization Techniques in BEMS for a Set of Commercial Buildings
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**Ref.** & **Control Techniques** & **Benefits** & **Drawbacks** & **Observations** \\ \hline
[9] & Internet of Energy (IoE) & Maximizing energy efficiency by minimizing losses and environmental impact using IoE. & The requirement of big data processing and large storage. & 21\% of energy loads can be deducted with significant cost reduction and energy saving. \\ \hline
[11] & Combination of machine-learning and model-based control approach. & Considered all physical characteristics of the building and human comfort compared to other researchers. & There is no real implementation and performance of the proposed system, which is only comparable to theoretical ideas. & Reduces the consumption of energy by 8–18\%. \\ \hline
[117] & DDQN & An aggregation controller has been used which aggregates all ILs in the system and remotely reads and interrupts the ILs. & There is less consideration of user preferences and comfort satisfaction. & Decreases the operating cost by 16.9\% in a day. \\ \hline
[118] & ANN and MILP & Smart controlling to manage the loads efficiently. & Longer computation time. & Reduction of up to 12.5\% of energy consumption and 10\% improvement in peak demand. \\ \hline
[119] & MILP with PV and ESS & During 90\% of the peak tariff, consumers can sell electricity to the grid. & If all consumers were motivated to buy in the same period, the demand may have increased dramatically. & Reducing the flexible loads by 40\% while saving 30\% of overall costs. \\ \hline
[124] & Rule-based algorithm & Strong control reliability and system reduces significant power. & The number of people detected in the room and consumption rate are not considered. & Savings of 23.5 KWh and USD 2.898 in total daily energy consumption. \\ \hline
[137] & DANN & Synthetic load profile generator is a robust and adaptable solution. & Slow convergence and longer computational time. & Achieved an average RMSE value of 111W and coefficient of determination is 97.5\%. \\ \hline
[138] & QL-PLR using RL & Higher convergence rate. & Consumer preference was not prioritized. & The system can reduce peak load demand by 9.28\%. \\ \hline
[167] & ANN and MPC & The next-day electricity price is provided to EMA to optimize the energy cost by controlling the heat pump. & There is a variation that may introduce the disturbance of human comfort. & Reduces energy costs by 14.8\%, when only heat pumps are used. \\ \hline
[168] & ANN and MPC & Provides good forecasting results compared to real assumptions with fewer error percentages. & The system will be required to investigate variable loads. & The system can sell energy to the grid for EGP 3.2 (Egyptian pound) within one day. \\ \hline
[169] & GA, ANN, and MPC & When loads are shifted within TOU, the results of energy savings increase by 27\%. & The authors have considered 100\% accurate forecasting which is not possible in a real scenario. & A total of 25\% energy savings. \\ \hline
[171] & Flexibility envelop concept and MPC & The MPC-based schemes increase the self-sufficiency of buildings. & No consideration of any forecasting error which is impossible in real cases. & 16\% cost savings and 10\% emission savings in the winter season, whereas in the summer, they were 26\% and 29\%, respectively. \\ \hline
[172] & MILP and MPC & Attempt to maintain energy consumption below the expected consumption for purpose of balancing. & HVAC model was out of the present work. & Saves approximately 125 KWh of net energy. \\ \hline
[173] & MG-EMS & Scalability, reliability, and extensibility. & It is implemented for residential use as one room at a time able to connect with solar energy. & The main power grid’s peak energy demand is reduced by 30.6\%. \\ \hline
[174] & EMS-in-Bs & Each function is critically synthesized by sub-function. & There is no clear direction in which methods might be preferable for BEMS. & \
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