# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
## 3 Big Data Importance in EMS  
Big data is instrumental in the transformation of smart power systems and their EMS, providing capabilities and insights to improve grid efficiency, reliability, and sustainability. Utilities can perform real-time monitoring and predictive analytics to anticipate and resolve potential issues before they escalate by leveraging vast amounts of data generated from diverse sources such as smart meters, sensors, and grid management systems. By optimizing energy distribution, demand forecasting, and the integration of RESs, advanced data analytics and ML algorithms minimize carbon footprints and reduce operational costs. Additionally, big data enables more effective demand response strategies and personalized energy services through comprehension of consumer behavior and energy consumption patterns, thereby facilitating improved decision-making. Big data will remain a fundamental component in the enhancement of the intelligence and resilience of modern energy infrastructures as the complexity of power systems increases. Consequently, big data can be considered in four sectors:  
* Energy Provider. * City and Building Construction Corporations. * Investors. * Authorities. Energy providers and utilities must provide intelligent and appealing energy services as the energy sector becomes more competitive. By utilizing smart meter and sensor technologies, weather information, demographic data, and occupant feedback, it is possible to provide rapid feedback on the value of innovative pricing programs that are contingent upon customer utilization patterns. Consumer behavior toward energy efficiency can be altered, and intelligent home automation services can be enabled by user-centered applications that transform smart meter data into value for building occupants. Energy providers and utilities can enhance revenue recovery and reduce costs by optimizing data collection \begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**Simulation Tools** & **Objectives** & **Exemplary Application** \\ \hline MATLAB/SIMULINK & MATLAB is a computational software used in science and engineering such as modeling battery behavior, analyzing system performance, and developing control algorithms, including those for BES. & [145; 146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156] \\ \hline HOMER Pro & Simulation software to assess the economics and performance of microgrids, select ESS, evaluate RES, and optimize microgrids. & [157] \\ \hline MAGNET-Infolytica & It facilitates the design and analysis of electromagnetic devices such as batteries, which assists in effectively optimizing BESS. & [158] \\ \hline GAMS & The optimization modeling language is employed to address intricate issues in a variety of domains, such as BESS design. It provides optimization capabilities, flexible model formulation, and integration with other software tools. & [159] \\ \hline PSCAD & The software is intended for the analysis of the dynamics and stability of electrical grids in the context of power system simulation. & [160; 161; 162] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation tools used in optimal analytics of smart grids’ EMS. processes. Numerous environmental and climate assessment procedures (ECAPs) have been submitted by signatories to the Covenant of Mayors for Climate and Energy [163], a global initiative of cities and towns dedicated to transparent climate action, outlining their strategies for achieving long-term CO2 reduction objectives [164; 165]. Energy performance certificates must be collected by EU countries through the use of a variety of energy assessment instruments and procedures. Nevertheless, this data is frequently stored in multiple formats and dispersed databases, which requires a distinctive representation for cross-country and cross-domain analyses.