Optimization Control Strategies in BEMS: Comparison
View Latest Version
Please note this is a comparison between Version 5 by Jessie Wu and Version 10 by Jessie Wu.
Due to rising energy demands, the industrial revolution has brought with it a slew of new issues. This phenomenon fosters the development of more resource-efficient control approaches. The building sector has a huge potential to mitigate energy demand using intelligent energy management systems (IEMS) and the concept of the internet of energy (IoE). The IoE combines features of a smart grid and the internet of things (IoT). The IoT refers to internet-based architecture in two ways: communication allows the system to be monitored and controlled in real-time via cloud computing or another internet service. It is proven that the potential of IoE-based building energy management systems (BEMS) will enhance the performance of future building energy utilization. The key idea of BEMS is monitoring and controlling the energy consumption in buildings with the aim of reducing emissions. The design of BEMS has taken into account factors such as efficiency, scalability, robustness, flexibility, and an ability to sense the environment and make decisions autonomously.

 

  • energy
  • demand
  • BEMS
  • IEMS
  • IoE
  • optimization
  • intelligent controls
  • commercial buildings

1. Uses of Intelligent Controls in Building Energy Management MSystems

Several research works have been carried out on different intelligent methods for building energy management systems (BEMS), and the most used are categorized as: (i) learning based methods of the AI domain, including a support vector machine (SVM) [1][2][128,129], K-nearest neighbor (K-NN) [1][128], artificial neural network (ANN) [3][130], reinforce learning (RL) [4][131], etc.; and (ii) model-based methods, such as the model predictive control (MPC) technique [5][132].
A learning-based control model where self-scheduling loads and the energy storage systems (ESS) of the building ensure the maximum usage of photovoltaic (PV)PV power, curtailment load profiles, and a reduced energy cost. The optimization algorithm optimizes the building cost and minimizes the fluctuation between PV generation and energy consumption, relying on predicted control information using a machine-learning algorithm [6][11]. In addition, it demonstrates the benefit of combining model-based and learning-based control methods into a single management framework for controlling multiple aspects of building performance [6][7][11,133].
The predictive algorithm model of energy consumption in smart buildings with a Microsoft Azure cloud-based machine learning platform wherein three methodologies namely SVM, K-NN, and artificial neural network (ANN) have been adopted for comparison to find the most accurate prediction model as compared to actual demand. Notably, the collected data is partitioned between 70% training data and 30% testing data. Though it took the most time to train, SVM was able to predict more accurately than the other two. The K-NN approach, on the other hand, was faster at completing the training, and ANN was faster than SVM, but it took many hours. However, a high training time is required for the SVM model to achieve a higher performance [8][134]. Most researchers focus on maintaining the indoor comfort of the buildings by implementing complexity prediction algorithms with higher cost and computational time, and power consumption. The model is carried out and investigated on five standard supervised machine learning models: polynomial regression (PR), support vector regression (SVR), random forest (PF), extreme gradient boost (XGB), and neural network (NN). In contrast, the best result output is shown for the SVR model with a tiny prediction error. A trustworthy NN model needs extensive data training set input and more processing time, while the amount of data is relatively small in the proposed system application, so it is rational to avoid the NN [9][135].
A novel deep neural-network-based algorithm for the day-ahead hourly energy consumption profile prediction of residential and commercial buildings in terms of occupancy rate and seasonality was investigated. A survey was conducted by a business manager, an electrical engineer, and a data scientist about all machine learning techniques, in which deep ANN achieved the highest score (multi-criteria analysis = 189) with a high accuracy prediction rate of about 98%. Firstly, training data was generated using synthetic load generation, where 100,000 data set points (load profiles) were produced for each occupancy rate type. The proposed forecasting model introduced these data to train an eight-layer deep neural network-based model and made decisions based on a limited number of inputs, evaluated using root mean square error (RMSE) and the coefficient of determination metrics [10][136].
Q-learning-based peak load reduction (QL-PLR) uses RL to present the optimal residential energy management (REM), which can decline only peak load demand and is associated with the dissatisfaction of consumers [11][137]. The designation of a dueling deep Q network (DDQN) captures the real-time state of the grid, demand-side strategy for interruptible loads (IL), along with the safe limit of regulating the system voltage and a reduction in peak demand loads, and the operation cost of distribution system operators (DSO). It is noteworthy that deep reinforcement learning (DRL) is adopted with DDQNs to optimize the DR management of IL under the TOU tariff and variable patterns of electricity consumption. To obtain the long-term profit for the DR management problem of IL, the Markov decision process (MDP) was formulated and solved using a DDQN-based DRL algorithm [7][133]. In addition, in comparison to a rule-based control (RBC) policy, DRL may also be utilized to control thermal storage in commercial buildings and can lower system operating costs by more than 50% [133][71].
MPC has a high level of efficiency in a building’s energy management. On the other hand, finding a good control-oriented model for MPC is a difficult task. To address this issue, data-driven models are applied to MPC tasks that have universal approximation capabilities [12][138]. To improve a building’s performance and take advantage of the TES’s operating flexibility, an MPC technique was devised. The findings revealed that having a TES in a commercial building allows for more flexibility in participating in DR programs, resulting in lower energy costs and demand charges while maintaining occupants’ comfort [13][139]. In the presence of an ESS, MPC was utilized to control the functioning of a commercial building in DR programs for bi-directional power flow with the main grid. The results of this researchtudy demonstrated that by exploiting the flexibility of the HVAC system, MPC resulted in a reduction in building running costs as well as an improvement in power grid performance [14][140].

2. Uses of Optimization Algorithms in Building Energy Management MSystems

In designing low-energy buildings, mathematical optimization can be used as a powerful tool to minimize the consumption of energy, while continuous and discrete issues are the two primary types of optimization challenges [15][16][141,142]. These problems can be formulated as binary, integer, or mixed integer optimization problems. Optimization algorithms are applied to solve these types of problems in various engineering fields [17][143].
A brief summary of the most selected optimization algorithms applied to BEMS problems is as follows:

2.1. Ant Colony Optimization (ACO)

ACO was motivated by observations of ant behavior [18][144]. This approach was originally created to handle discrete optimization problems before being expanded to include continuous variables. ACO for the continuous domain is the name of the extension (ACOR). ACOR showed better performance in finding the best solution as compared to other benchmark algorithms: particle swarm optimization with inertia weight (PSOIW), hybrid particle swarm optimization and Hooke–Jeeves (PSO-HJ), and the Nelder–Mead (NM) algorithm [16][142]. An ACO load scheduling strategy for a smart home was proposed. The goal was to achieve the best possible use of integrated renewable energy sources. This is accomplished by concentrating on the total electricity bill, TOU, and the overall improved quality of life (QoL) [19][145].

2.2. Artificial Bee Colony (ABC)

ABC is an optimization algorithm based on honey bee foraging behavior [20][146]. In [21][147], a HEMS for household appliances was proposed by implementing DR schemes for residential consumers with facilitating renewable energy integration. This framework was solved based on an improved ABC algorithm. In [22][148], the authors proposed a new approach of ABC with Knowledge Base (ABC-KB) for the management of power and the occupant’s preferred environment inside a residential building. ABC-KB uses less power than GA and PSO.

2.3. Particle Swarm Optimization (PSO)

PSO is an optimization technique based on the social behavior of bird flocking or fish schooling [23][149]. The conventional continuous PSO algorithm was modified to binary spaces, while BPSO is a binary variant of PSO. It is a bird-inspired optimization technique based on flocks of birds looking for food. Birds move in certain locations and velocities when foraging for food [24][150]. In [25][151], they adopted and developed cooperative particle swarm optimization (CPSO) to optimize user comfort and the electricity bills of individual homes as well as avoiding peak loads and peak rebounds on the grid. In [26][152], they provided a regularized PSO algorithm for optimally controlling battery energy in a grid-connected microgrid, lowering power costs.

2.4. Genetic Algorithm (GA)

GA is an iterative optimization technique inspired by live creatures’ genetic processes. New genes are created that inherit the characteristics of their parents [27][153]. Chromosome representation and algorithmic flows are the two main components of a GA. An algorithmic flow is an iterative technique for generating and evolutionarily selecting chromosomes to obtain high-quality solutions, while a chromosomal representation is a scheme for modeling a solution [28][154]. GA was used to optimize the scheduling of ESS and plug-in electric vehicle (PEV) operations in a home energy management strategy in order to reduce daily electrical energy expenditure for the user [29][155]. Residential load management solutions may necessitate appliance scheduling to achieve specific goals such as load factor reduction, a PAR ratio reduction, or energy cost reduction. This problem is solved using GA [30][156].

2.5. Other Optimization Algorithms

In [31][157], the proposed hybrid algorithms have better performance and faster convergence compared to a single-based algorithm. The hybrid-based algorithms such as bacterial foraging optimization (BFO), gray wolf optimization (GWO), wind-driven optimization (WDO), enhanced differential evolution (EDE), and the harmonic search (HS) algorithm can solve the DSM optimization problems in SG. A detailed summary of other optimization algorithms is illustrated in Table 1.
Table 1.
Summary of others optimization approaches in BEMS.
Optimization Techniques Objectives Contributions Ref.
HEDE based on EDE and HSA Cost, PAR, and user comfort optimization. EDE performance is better than HSA in terms of cost reduction and HSA is better in terms of PAR as compared to EDE. [32][158]
Dijkstra algorithm (DA) Consumption cost, curtailed loads, grid imbalances, and used energy mixes optimization. DA reduced 51.72% of the cost when attaching RER and 10.22% of PAR. [33][159]

Energy Management SystemsMS for a Commercial Building

Building optimization problems are considered MILP problems that have been solved using MPC [40][117]. Intelligent controlling has been used to manage loads efficiently and an ANN strategy has been adopted to maintain a comfort zone in the building for the occupant, with an MILP scheduling technique to decay the peak demand of consumer [41][166]. An energy management agent (EMA) consists of an ANN and MPC for the modeling and optimization of building flexibility. The Monte Carlo Tree search-based planning and control was used to find the optimal policies with an ANN. The system can predict the demand for a day ahead and has a tiny prediction error [42][167]. The proposed system consisted of an ANN and MPC. In contrast, an ANN was used for renewable energy (i.e., solar and wind) forecasting and ensured the optimized usages of generated energy, and MPC is adopted for intelligent home control [43][168].
The ANN is used to accurately predict power consumption and indoor temperature selection by given weather, occupancy, and temperature setpoints as input. At the same time, a GA has been taken to adapt to the ANN to minimize energy consumption, and an optimization control strategy was assessed in case of the day ahead and MPC [44][169].
The uncertainty of environmental variables and users’ preferences has been tackled using a data-driven machine-learning approach. Furthermore, a lifelong multitask framework was adopted to exploit structural similarities in control policies as there were different room sizes in buildings. Kernel-based learning was pursued as well to mitigate the non-linearity policy. Finally, a dual decomposition method was employed to cope with DR constraints across the spaces, transforming the overall problem into a series of unconstrained stochastic optimization problems for individual rooms. The method was verified via numerical experimental based on semi-real data sets [45][170].
Three artificial intelligence techniques were used to solve the problem of energy demand planning in smart homes. First, the modification of the elitist none-dominated sorting genetic algorithm II to demand-side management was applied and accounted for electricity fluctuations over time, priority in the use of equipment, operating cycles, and a battery bank. Second, the forecast of demand-side consumers, distribution generation, renewable energies, and weather for a day ahead from the nearest meteorology office was considered for demand-side management by employing the SVR technique. Third, the k determined user comfort levels through the cluster technique [46][171]. Table 2 shows a full explanation of each optimization control technique, along with its benefits and drawbacks.
Table 2.
A comparison of the most commonly used optimization methods in BEMS.
Ref. Control Techniques Benefits Drawbacks Observations
[47][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.
[6][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%.
Satin bowerbird optimization (SBO)

algorithm		 To optimize the scheduling of appliances within a discrete comfort window (DCW). Reduced the electricity cost from ₹29.14/day to ₹22.84/day, and reduction of PAR is 10.28%. [
[40][117]34][160]
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. Cuckoo search optimization (CSO)

algorithm		 Reduction in electricity cost, PAR, and minimum user waiting time.
[48CSOA is superior in terms of cost and PAR compared to CSA and GA. ][118][ ANN and MILP35][ Smart controlling to manage the loads efficiently.161]
Longer computation time. Reduction of up to 12.5% of energy consumption and 10% improvement in peak demand. Hybrid genetic particle swarm optimization (HGPO) algorithm Optimization of electricity bills, carbon emissions, user comfort, PAR. Reduction of electricity cost by 25.55%, PAR by 36.98%, and carbon emissions by 24.02%, respectively.
[49][119] MILP with PV and ESS[36][162 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. Genetic BPSO (GBPSO)

algorithm		 Electricity bills and PAR optimization. GBPSO is better in terms of both cost reduction and curtailment of PAR compared to GA, BPSO, WDO, and BFOA. [37
[50][124] Rule-based algorithm][163 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. Binary backtracking search (BBS)

algorithm		 Reducing and scheduling energy usage. BBSA provides better performance compared to BPSO, in terms of reducing energy consumption, total electricity bills, and saving the energy of certain loads at peak hours. [38][164]
[11][137] DANN Harmony search gray wolf optimization (HSGWO) algorithm. Efficient scheduling HSGWO performs better than HSA and GWO in terms of cost and user comfort. [39][165]

2.6. Neighborhood Energy Optimization Algorithms for a Set of Commercial Buildings

A single subject owns a number of the neighborhood’s buildings in the single-owner scenario. The architecture for energy optimization is centralized in design. The neighborhood buildings are owned by various subjects in the multi-owner scenario, each of whom seeks to reduce their individual energy costs. The building owners additionally consent to provide flexibility to the entire neighborhood and run their own local generation and storage units in a coordinated manner to achieve neighborhood-level goals. The architecture for energy optimization is hierarchical. A centralized optimization for a single-owner neighborhood with a high level of transparency and a hierarchical two-level optimization for a neighborhood with multiple owners and a reduced level of transparency are two separate optimization algorithms. Additionally, in order to lower the neighborhood net load as viewed from the grid side and maintain the neighborhood pollution emissions below a predetermined threshold, the neighborhood energy optimization algorithms schedule the generation and storage equipment based on energy prices. It has been demonstrated that the use of flexible resources, such as thermal storage (related to thermal comfort levels) or electrical storage, enables one to pursue an economic goal while utilizing the flexibility of the local energy supply.

3. The Impact of Dual Optimization Techniques in Building

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%.
[
12
][138] QL-PLR using RL Higher convergence rate. Consumer preference was not prioritized. The system can reduce peak load demand by 9.28%.
[42][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.
[43][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.
[44][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.
[46][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.
[51][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.
[52][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%.
[53][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. “Control-optimize” achieves the highest energy saving rates of around 30% compared to “estimate-predict” with 10%.

4. The Impact of Dual Optimization Techniques in Building Energy Management SystemsMS for a Set of Commercial Buildings

Neighborhoods or districts are not frequently included in the application of optimum control ideas to achieve energy efficiency in buildings and the optimal exploitation of regional resources. In [171][462], they consider a neighborhood with several buildings that have agreed to coordinate how they use their energy loads and resources in order to achieve some overall objectives, while still allowing for the pursuit of individual optimization goals. When this occurs, a building’s local resources should work together with a top-level optimization engine to balance the accomplishment of local optimization goals with neighborhood-level goals. A hierarchical optimization algorithm was introduced to divide the optimization at the building level and the neighborhood level in such a way that the bottom level managed the individual building objectives and the top level addressed the neighborhood-level objective, in order to address the most general case of multiple ownership neighborhoods. In particular, the building-level energy management would give the top-level optimizer flexibility and provide recommendations on control measures to implement to move the neighborhood closer to achieving its objectives [172][512]. The optimistic assumption in bilevel optimization (i.e., the two layers of optimization tasks are nested one inside the other) states that the consumers choose the best option that benefits the retailer the most. The pessimistic variant, on the other hand, deals with the scenario in which the consumers give the retailer their least preferred optimal response, protecting against potential losses brought on by an unexpected choice. However, the work in [173][523] shows the formal properties of the best solutions to a bilevel tariff optimization issue for both the computationally challenging general case with an arbitrary number of consumers and the particular case with an easily tractable single consumer. The key relevance of these findings is that by perturbing the issue data and the optimal price vector, the pessimistic variant may be reduced to the optimistic one, which also yields the first effective solution technique for the pessimistic version. On the other hand, a numerical case study was offered to show that, if consumers do not select their optimal solution as expected, addressing the optimistic problem could directly result in a significant loss of profit for the retailer. In [174][534], they emphasize the distinction between the optimistic and pessimistic versions of the bilevel optimization problem with regard to energy management.

 

References

  1. Krishna Prakash, N.; Prasanna Vadana, D. Machine Learning Based Residential Energy Management System. In Proceedings of the 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India, 14–16 December 2017. G. Zsembinszki et al., “Deep Learning Optimal Control for a Complex Hybrid Energy Storage System,” 2021, doi: 10.3390/buildings.
  2. Moradzadeh, A.; Mohammadi-Ivatloo, B.; Abapour, M.; Anvari-Moghaddam, A.; Roy, S.S. Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis. IEEE Access 2022, 10, 2196–2215. L. Cupelli, M. Schumacher, A. Monti, D. Mueller, L. de Tommasi, and K. Kouramas, “Chapter four - Simulation Tools and Optimization Algorithms for Efficient Energy Management in Neighborhoods,” 2017. doi: 10.1016/B978-0-12-809951-3/00004-1.
  3. Hafeez, G.; Alimgeer, K.S.; Wadud, Z.; Khan, I.; Usman, M.; Qazi, A.B.; Khan, F.A. An innovative optimization strategy for efficient energy management with day-ahead demand response signal and energy consumption forecasting in smart grid using artificial neural network. IEEE Access 2020, 8, 84415–84433. T. Kis, A. Kovács, and C. Mészáros, “On optimistic and pessimistic bilevel optimization models for demand response management,” Energies (Basel), vol. 14, no. 8, Apr. 2021, doi: 10.3390/en14082095.
  4. Dong, C.; Sun, J.; Wu, F.; Jia, H. Probability-Based Energy Reinforced Management of Electric Vehicle Aggregation in the Electrical Grid Frequency Regulation. IEEE Access 2020, 8, 110598–110610. M. J. Alves and C. H. Antunes, “A semivectorial bilevel programming approach to optimize electricity dynamic time-of-use retail pricing,” Comput Oper Res, vol. 92, pp. 130–144, Apr. 2018, doi: 10.1016/j.cor.2017.12.014.
  5. Sturzenegger, D.; Gyalistras, D.; Morari, M.; Smith, R.S. Model predictive climate control of a swiss office building: Implementation, results, and cost--benefit analysis. IEEE Trans. Control Syst. Technol. 2015, 24, 1–12.
  6. Eini, R.; Linkous, L.; Zohrabi, N.; Abdelwahed, S. Smart building management system: Performance specifications and design requirements. J. Build. Eng. 2021, 39, 102222.
  7. Wang, B.; Li, Y.; Ming, W.; Wang, S. Deep Reinforcement Learning Method for Demand Response Management of Interruptible Load. IEEE Trans. Smart Grid 2020, 11, 3146–3155.
  8. Shapi, M.K.M.; Ramli, N.A.; Awalin, L.J. Energy consumption prediction by using machine learning for smart building: Case study in Malaysia. Dev. Built Environ. 2021, 5, 100037.
  9. Zhang, X.; Pipattanasomporn, M.; Chen, T.; Rahman, S. An IoT-Based Thermal Model Learning Framework for Smart Buildings. IEEE Internet Things J. 2020, 7, 518–527.
  10. Truong, L.H.M.; Chow, K.H.K.; Luevisadpaibul, R.; Thirunavukkarasu, G.S.; Seyedmahmoudian, M.; Horan, B.; Mekhilef, S.; Stojcevski, A. Accurate prediction of hourly energy consumption in a residential building based on the occupancy rate using machine learning approaches. Appl. Sci. 2021, 11, 2229.
  11. Kumari, A.; Tanwar, S. Al-based Peak Load Reduction Approach for Residential Buildings using Reinforcement Learning. In Proceedings of the 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, 19–20 February 2021; pp. 972–977.
  12. Cao, Y.; Du, J.; Soleymanzadeh, E. Model predictive control of commercial buildings in demand response programs in the presence of thermal storage. J. Clean. Prod. 2019, 218, 315–327.
  13. Wang, J.; Li, S.; Chen, H.; Yuan, Y.; Huang, Y. Data-driven model predictive control for building climate control: Three case studies on different buildings. Build. Environ. 2019, 160, 106204.
  14. Razmara, M.; Bharati, G.R.; Hanover, D.; Shahbakhti, M.; Paudyal, S.; Robinett, R.D., III. Building-to-grid predictive power flow control for demand response and demand flexibility programs. Appl. Energy 2017, 203, 128–141.
  15. Si, B.; Tian, Z.; Jin, X.; Zhou, X.; Tang, P.; Shi, X. Performance indices and evaluation of algorithms in building energy efficient design optimization. Energy 2016, 114, 100–112.
  16. Bamdad, K.; Cholette, M.E.; Guan, L.; Bell, J. Ant colony algorithm for building energy optimisation problems and comparison with benchmark algorithms. Energy Build. 2017, 154, 404–414.
  17. Akay, B.; Karaboga, D.; Gorkemli, B.; Kaya, E. A survey on the Artificial Bee Colony algorithm variants for binary, integer and mixed integer programming problems. Appl. Soft Comput. 2021, 106, 107351.
  18. Dorigo, M.; Stützle, T. Ant colony optimization: Overview and recent advances. In Handbook of Metaheuristics; Springer: Berlin/Heidelberg, Germany, 2019; pp. 311–351.
  19. Okonta, C.I.; Kemp, A.H.; Edopkia, R.O.; Monyei, G.C.; Okelue, E.D. A heuristic based ant colony optimization algorithm for energy efficient smart homes. In Proceedings of the 5th International Conference & Exhibition on Clean Energy, Montreal, QC, Canada, 22–24 August 2016; pp. 1–12.
  20. Zhou, G.; Moayedi, H.; Bahiraei, M.; Lyu, Z. Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings. J. Clean. Prod. 2020, 254, 120082.
  21. Zhang, Y.; Zeng, P.; Zang, C. Optimization algorithm for home energy management system based on artificial bee colony in smart grid. In Proceedings of the 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, China, 8–12 June 2015; pp. 734–740.
  22. Wahid, F.; Ghazali, R.; Ismail, L.H. An Enhanced Approach of Artificial Bee Colony for Energy Management in Energy Efficient Residential Building. Wirel. Pers. Commun. 2019, 104, 235–257.
  23. Niazi, G.; Lalwani, M. PSO based optimal distributed generation placement and sizing in power distribution networks: A comprehensive review. In Proceedings of the 2017 International Conference on Computer, Communications and Electronics (Comptelix), Jaipur, India, 1–2 July 2017; pp. 305–311.
  24. Naseem, M.; Abid, S.; Khalid, R.; Hafeez, G.; Mahboob, S. Towards Heuristic Algorithms: GA, WDO, BPSO, and BFOA for Home Energy Management in Smart Grid. In Advances on Broad-Band Wireless Computing, Communication and Applications; Springer: Berlin/Heidelberg, Germany, 2018; pp. 267–278.
  25. Zhu, J.; Lauri, F.; Koukam, A.; Hilaire, V. Scheduling optimization of smart homes based on demand response. In Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations, Bayonne, France, 14–17 September 2015; pp. 223–236.
  26. Hossain, M.A.; Pota, H.R.; Squartini, S.; Abdou, A.F. Modified PSO algorithm for real-time energy management in grid-connected microgrids. Renew. Energy 2019, 136, 746–757.
  27. Mirjalili, S. Genetic algorithm. In Evolutionary Algorithms and Neural Networks; Springer: Berlin/Heidelberg, Germany, 2019; pp. 43–55.
  28. Lin, C.-S.; Lee, I.-L.; Wu, M.-C. Merits of using chromosome representations and shadow chromosomes in genetic algorithms for solving scheduling problems. Robot. Comput. Integr. Manuf. 2019, 58, 196–207.
  29. Liemthong, R.; Srithapon, C.; Ghosh, P.K.; Chatthaworn, R. Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs. Energies 2022, 15, 537.
  30. Liemthong, R.; Srithapon, C.; Ghosh, P.K.; Chatthaworn, R. Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs. Energies 2022, 15, 537S.
  31. Sarker, E.; Halder, P.; Seyedmahmoudian, M.; Jamei, E.; Horan, B.; Mekhilef, S.; Stojcevski, A. Progress on the demand side management in smart grid and optimization approaches. Int. J. Energy Res. 2021, 45, 36–64.
  32. Khan, Z.A.; Zafar, A.; Javaid, S.; Aslam, S.; Rahim, M.H.; Javaid, N. Hybrid meta-heuristic optimization based home energy management system in smart grid. J. Ambient Intell. Humaniz. Comput. 2019, 10, 4837–4853.
  33. Ullah, I.; Rasheed, M.B.; Alquthami, T.; Tayyaba, S. A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid. Sustainability 2020, 12, 184.
  34. Chellamani, G.K.; Chandramani, P.V. An optimized methodical energy management system for residential consumers considering price-driven demand response using satin bowerbird optimization. J. Electr. Eng. Technol. 2020, 15, 955–967.
  35. Aslam, S.; Iqbal, Z.; Javaid, N.; Khan, Z.A.; Aurangzeb, K.; Haider, S.I. Towards efficient energy management of smart buildings exploiting heuristic optimization with real time and critical peak pricing schemes. Energies 2017, 10, 2065.
  36. Imran, A.; Hafeez, G.; Khan, I.; Usman, M.; Shafiq, Z.; Qazi, A.B.; Khalid, A.; Thoben, K.-D. Heuristic-based programable controller for efficient energy management under renewable energy sources and energy storage system in smart grid. IEEE Access 2020, 8, 139587–139608.
  37. Javaid, N.; Naseem, M.; Rasheed, M.B.; Mahmood, D.; Khan, S.A.; Alrajeh, N.; Iqbal, Z. A new heuristically optimized Home Energy Management controller for smart grid. Sustain. Cities Soc. 2017, 34, 211–227.
  38. Ahmed, M.S.; Mohamed, A.; Khatib, T.; Shareef, H.; Homod, R.Z.; Abd Ali, J. Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm. Energy Build. 2017, 138, 215–227.
  39. Nazeer, O.; Javaid, N.; Rafique, A.A.; Kiani, S.; Javaid, Y.; Khurshid, Z. Home energy management using hybrid meta-heuristic optimization technique. In Innovative Mobile and Internet Services in Ubiquitous Computing; Springer: Berlin/Heidelberg, Germany, 2018; pp. 599–609.
  40. Costanzo, G.T.; Zhu, G.; Anjos, M.F.; Savard, G. A system architecture for autonomous demand side load management in smart buildings. IEEE Trans. Smart Grid 2012, 3, 2157–2165.
  41. Latifah, A.; Supangkat, S.H.; Ramelan, A. Smart Building: A Literature Review. In Proceedings of the 7th International Conference on ICT for Smart Society (ICISS), Bandung, Indonesia, 19–20 November 2020.
  42. Kiljander, J.; Sarala, R.; Rehu, J.; Pakkala, D.; Paakkonen, P.; Takalo-Mattila, J.; Kansala, K. Intelligent Consumer Flexibility Management with Neural Network-Based Planning and Control. IEEE Access 2021, 9, 40755–40767.
  43. Megahed, T.F.; Abdelkader, S.M.; Zakaria, A. Energy Management in Zero-Energy Building Using Neural Network Predictive Control. IEEE Internet Things J. 2019, 6, 5336–5344.
  44. Reynolds, J.; Rezgui, Y.; Kwan, A.; Piriou, S. A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control. Energy 2018, 151, 729–739.
  45. Kim, S.; Mowakeaa, R.; Kim, S.J.; Lim, H. Building energy management for demand response using kernel lifelong learning. IEEE Access 2020, 8, 82131–82141.
  46. Rocha, H.R.O.; Honorato, I.H.; Fiorotti, R.; Celeste, W.C.; Silvestre, L.J.; Silva, J.A.L. An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes. Appl. Energy 2021, 282, 116145.
  47. Hannan, M.A.; Faisal, M.; Ker, P.J.; Mun, L.H.; Parvin, K.; Mahlia, T.M.I.; Blaabjerg, F. A review of internet of energy based building energy management systems: Issues and recommendations. IEEE Access 2018, 6, 38997–39014.
  48. Arun, S.L.; Selvan, M.P. Intelligent Residential Energy Management System for Dynamic Demand Response in Smart Buildings. IEEE Syst. J. 2018, 12, 1329–1340.
  49. Esmaeel Nezhad, A.; Rahimnejad, A.; Gadsden, S.A. Home energy management system for smart buildings with inverter-based air conditioning system. Int. J. Electr. Power Energy Syst. 2021, 133, 107230.
  50. Shareef, H.; Al-Hassan, E.; Sirjani, R. Wireless home energy management system with smart rule-based controller. Appl. Sci. 2020, 10, 4533.
  51. Nagpal, H.; Avramidis, I.I.; Capitanescu, F.; Heiselberg, P. Optimal energy management in smart sustainable buildings—A chance-constrained model predictive control approach. Energy Build. 2021, 248, 111163.
  52. Zhang, C.; Wu, W.; Huang, H.; Yu, H. Fair energy resource allocation by minority game algorithm for smart buildings. In Proceedings of the 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany, 12–16 March 2012; pp. 63–68.
  53. Al-Ghaili, A.M.; Kasim, H.; Al-Hada, N.M.; Jorgensen, B.N.; Othman, M.; Jihua, W. Energy Management Systems and Strategies in Buildings Sector: A Scoping Review. IEEE Access 2021, 9, 63790–63813.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback