## 1 Introduction  
The emergence of new technologies, such as automated transport, digitalization, e-maintenance, IoT, cloud computing, big data analytics, virtual reality (VR), and Industry 4.0 concepts, has opened avenues for more the efficient and sustainable management of Critical Infrastructures (CIs) [1, 2, 3]. In particular, recent advancements in machine learning, particularly in the field of artificial neural networks (ANNs), have shown great promise in overcoming the limitations of traditional approaches and improving their performance [4, 5, 6]. For instance, [PERSON] and [PERSON] [7] have used ANNs to estimate limit state functions in nonlinear structural analysis using the response surface method. [PERSON] et al. [8]employed deep learning techniques for vulnerability analysis of CIs. [PERSON] et al. [9] demonstrated the power of ANNs in improving accuracy in risk assessment of critical infrastructure. One of the primary objectives of this study is to demonstrate the efficacy of ANNs in the electric power load forecasting of CIs. Notably, research reports [10, 11] indicate that a reduction of 1% in power load prediction error can result in savings of up to GBP 10 million in electricity power operational costs. Thus, selecting the most suitable ANN model in power load forecasting can lead to substantial cost savings and contribute to a more sustainable power system. Moreover, with a better understanding of ANN's architects and performances, researchers and practitioners can find suitable applications for challenges inherent to the complex nature of CIs more efficiently and effectively. Ongoing research and development in ANN models continue to enhance their capabilities, providing opportunities for further improvements in the performance, reliability, and resilience of CIs. Load forecasting is typically categorized into three subcategories: short-term (hourly to a few weeks ahead), medium-term (beyond a week to a few months ahead), and long-term (forecasting consumption for the next year to a few years ahead) [12]. Short-term load forecasting (STLF) primarily deals with power distribution aspects such as unit commitment, load switching, load balancing, and dispatching. Medium-term load forecasting (MTLF) aids in the efficient scheduling of fuel supplies and proper operation and maintenance activities. Long-term load forecasting (LTLF) supports infrastructure development, including planning new power plants and distribution networks. Developing accurate forecasting models facilitates efficient and economical supply and demand management for both customers and suppliers, leading to a more sustainable power system. Numerous models, methods, and techniques have been developed in academia and industry for power load forecasting, broadly categorized as multi-factor forecasting methods and time series methods [13]. Time series methods further consist of statistical models, machine learning models, and hybrid models [13]. Conventionally, statistical models such as multiple linear regression [14, 15, 16], general exponential smoothing [17, 18, 19], and autoregressive integrated moving averages (ARIMA) have been widely used for power load forecasting [20, 21, 22]. These methods often require the time series data to be stationary (i.e., constant mean, variance, and serial correlation). While these approaches can handle univariate data, they are single-step forecast models that necessitate extensive preprocessing and explicit definitions of input characteristics [23]. Machine learning models such as Support Vector Machines (SVM) [24, 25, 26], Bayesian Belief Networks [20, 27], and Principal Component Analysis (PCA) [15, 28] have also been employed in power load forecasting. For instance, [PERSON] and [PERSON] [26] utilized SVM for short-term load forecasting and demonstrated that applying a threshold between the daily average load of training input patterns enhances the accuracy of SVM predictions.