## 2 Energy Management and Review of Surveys
### Optimization Techniques  
[PERSON] et al. [17] categorized the optimization methods in EM problems into dynamic programming, mixed integer programming (MIP) (mixed integer non-linear programming, mixed integer quadratic programming, and mixed integer linear programming), stochastic programming, non-linear programming, and integer programming. [PERSON] et al. [12] presented an overview of the numerical methodologies of EMS including classical (including LP, MILP, MINLP, NLP, DP, Approximate DP, Rule-based, Deterministic-based, and NP-Hard), metaheuristic (non-dominated sorting genetic algorithm II (NSGA-II), particle swarm optimization (PSO), confidence-based velocity-controlled particle swarm optimization (CVCPSO), multi-voxel pattern analysis (MVPA), grey wolf optimization (GWO), artificial bee colony (ABC), enhanced bee colony (EBC), adaptive differential evaluation (ADE), modified PSO (MOPSO), enhanced velocity differential evolutionary PSO (EVDEPSO), rule-based bat optimization (BO), crow search algorithm (CSA), gravitational search algorithm (GSA), genetic algorithm (GA), alternating direction method of multipliers (ADMM) using modified firefly algorithm (MFA), teaching-learning optimization (TLA), social spider algorithm (SSO), and whale optimization algorithm (WOA)), and intelligent (Fuzzy-based models: Neuro-fuzzy, ANN, and RNN; predictive models; game theory and deep learning). In addition, the authors presented a complete review of previous studies, based on the problems addressed, including communication and information exchange, cost minimization, data collection and scenario generation, demand response program, energy scheduling, forecasting-based, generating energy with lower emissions, market-participation-based, on the vehicle-to-grid system (V2G), operating time, optimal storage management, reliability of operation, stability analysis, and time response. [PERSON] et al. [15] addressed the optimization techniques for EM: linear optimization, HOMER analysis, genetic algorithm, energy scheduling optimization problem (ESOP), fast Fourier transform (FFT), charging/discharging algorithm, generalized reduced gradient (GRG) algorithm, forecasting method, P2P trading mechanism, NSGA-II (Non-dominated Sorting Genetic Algorithm-II), interval optimization, OPF (optimal power flow) technique auction algorithm CPLEX solver, LabVIEW analysis, and Newton-Raphson technique swarm intelligence approach. Furthermore, the optimization methods are compared based on their advantages and disadvantages, applications, and objectives. Among the optimization algorithms, we can mention the deterministic methods, including MILP, dynamic programming (DP), and MINLP; metaheuristic methods, including PSO, GA, and artificial fish swarm; artificial intelligence methods, including artificial neural network and Fuzzy logic; other methods such as Manta Ray optimization and Harris Hawks optimization. [PERSON] et al. [18] reviewed the relevant recent research to improve EM for smart buildings using artificial intelligence (AI) methods. For this purpose, they first introduced the concept of \"Autonomous Cycles of Data Analysis Tasks\" (ACODAT), which defines the need for an autonomous management system for some specialized tasks, including monitoring, analysis, and decision-making to reach defined objectives, such as the energy efficiency. They were previously applied in different domains, such as smart classrooms, smart cities, and industry. In addition, the methodology for the development of a data mining application (MIDANO) for implementing the ACODAT architectures is presented, which includes three phases: phase 1, which specifies the ACODAT for the problem to be solved; phase 2, which prepares the data for the data analytics tasks (i.e., the extraction and transformation operations of the data); phase 3, which implements all the data analytics tasks of the autonomic cycle.