A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and the Internet of things (IoT), where digitalization is at the core of the energy sector transformation. However, smart grids require that energy managers become more concerned about the reliability and security of power systems. Therefore, energy planners use various methods and technologies to support the sustainable expansion of power systems, such as electricity demand forecasting models, stochastic optimization, robust optimization, and simulation. Electricity forecasting plays a vital role in supporting the reliable transitioning of power systems. This paper deals with short-term load forecasting (STLF), which has become an active area of research over the last few years, with a handful of studies. STLF deals with predicting demand one hour to 24 h in advance. We extensively experimented with several methodologies from machine learning and a complex case study in Panama. Deep learning is a more advanced learning paradigm in the machine learning field that continues to have significant breakthroughs in domain areas such as electricity forecasting, object detection, speech recognition, etc. We identified that the main predictors of electricity demand in the short term: the previous week’s load, the previous day’s load, and temperature. We found that the deep learning regression model achieved the best performance, which yielded an R squared (R²) of 0.93 and a mean absolute percentage error (MAPE) of 2.9%, while the AdaBoost model obtained the worst performance with an R² of 0.75 and MAPE of 5.70%.

Predicting the future peak demand growth becomes increasingly important as more consumer loads and electric vehicles (EVs) start connecting to the grid. Accurate forecasts will enable energy suppliers to meet demand more reliably. However, this is a challenging problem since the peak demand is very nonlinear. This study addresses the research question of how deep learning methods, such as convolutional neural networks (CNNs) and long-short term memory (LSTM) can provide better support to these areas. The goal is to build a suitable forecasting model that can accurately predict the peak demand. Several data from 2004 to 2019 was collected from Panama’s power system to validate this study. Input features such as residential consumption and monthly economic index were considered for predicting peak demand. First, we introduced three different CNN architectures which were multivariate CNN, multivariate CNN-LSTM and multihead CNN. These were then benchmarked against LSTM. We found that the CNNs outperformed LSTM, with the multivariate CNN being the best performing model. To validate our initial findings, we then evaluated the robustness of the models against Gaussian noise. We demonstrated that CNNs were far more superior than LSTM and can support spatial-temporal time series data.

Multi-resolution modeling (MRM) includes many different approaches. It is very well known by the Distributed Simulation community that the High Level Architecture (HLA) is an architecture designed to facilitate interoperability and software reuse. Therefore, the unit of MRM is usually the federate. Multiresolution representation of entities consists in maintaining multiple and concurrent representations of entities. As such, several approaches may be used to manage the aggregation/disaggregation processes, according to the particular needs of the simulation exercised. However, we have found that there are many approaches presented in the literature. We have to weigh many considerations when comparing the different MRM approaches. This paper introduces the different approaches and provides an experiment using constructive simulation.

The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R² of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector’s dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R² value of 0.94 and a MAPE value of 4.8%.

IoT-based Smart Grids (SGs) are important to modern society. SGs can improve the profitability and reliability of the electric power system by incorporating renewable energies and highly developed communication technologies. The communication network plays an essential role in electrical networks, and trends favor implementing SGs with IoT devices. However, these IoT-based SGs are vulnerable to cyberattacks. This article presents our studies of malware that can attack IoT-based SGs. First, the article explains as a first step the conclusions of a literature survey on SGs complemented with a Delphi process with security experts to understand trends and malware with an emphasis on the IoT area. Next, the article discusses the behavior of the chosen malware using System Dynamics and calibration with stochastic optimization. Finally, conclusions are given, which identify research work to be carried out using more in-depth modeling with agent-based simulation and multiple resolution modeling (MRM). MRM can provide a platform to integrate with time, scale, and space specialized models of each system of the SGs to support the development of effective risk management schemes.

Predicting electricity demand in emerging economies is challenging as congested cities are exposed to unpredictable mobility patterns, weather sensitivity, and global pandemics such as COVID-19. Although mass public transportation systems have long supported urban mobility, limited understanding remains of how disruptions to these travel modes impact the energy sector. This paper introduces a framework that leverages metro ridership data, a point of interest, to enhance the predictive modeling of electricity demand. Convolutional neural networks (CNNs) are trained across various pandemic phases, enabling more effective mapping between multi-scale features and electricity demand. Results demonstrated that the model receiving historical data related to COVID-19 test positivity rates and metro users was more robust to the early impacts, achieving a MAPE of 3.35% and RMSE of 59.82. The research was validated through a case study of the Republic of Panama, featuring one of Latin America’s most modern rapid transit systems, which transports an average of 365,000 passengers daily to the city’s center as of 2025. A SHAP analysis further demonstrates that shifts in metro ridership patterns influenced model outcomes during the pandemic’s peak from 2020 to 2022.