Numerous advanced approaches and algorithms have been developed with the support of computational intelligence, aiming to enhance accuracy and efficiency across various fields of knowledge. These innovative methods enable more detailed and assertive data analysis, facilitating quick and well-grounded decision-making, bringing significant benefits to different areas of study and application. A growing body of studies and research has been published in this field, highlighting the increasing interest and significance of these approaches in estimating electricity consumption [6; 7; 8; 9; 10; 11; 12]. Building on this foundation, the present study demonstrates the efficacy of artificial neural networks (ANN) for modeling complex energy consumption data. The proposed approach extends beyond traditional methods and can be applied to smart buildings equipped with shared energy storage systems (SESS), a novel solution for efficiently regulating energy usage in both residential and commercial sectors. SESS technology offers lower installation and operational costs compared to individual storage systems, making it a viable option for enhancing energy management in modern buildings. Smart buildings that increasingly integrate distributed energy resources (DERs), such as solar photovoltaic systems and advanced smart grid technologies, highlight the growing demand for intelligent energy management systems [13]. For utility companies, accurate consumption forecasting enhances load management by reducing the need for additional generation capacity and allowing better integration of renewable energy sources, like solar and wind. This precise demand forecasting improves grid stability, lowers operational costs, and facilitates the inclusion of decentralized energy systems. From a consumer perspective, accurate energy projections promote strategic energy management, especially when combined with smart technologies such as time-of-use pricing and demand response models. Consumers benefit from direct cost savings by optimizing energy use during off-peak hours and improving overall energy efficiency in homes and businesses. Moreover, the insights gained from these forecasts encourage investments in energy-efficient technologies, such as energy storage systems and renewable generation options like rooftop solar panels [14; 15]. Energy consumption forecasting is crucial for optimizing energy systems, especially in residential settings with dynamic load patterns. Although this study primarily focuses on household energy demands, the proposed strategy can also be applied to other high-energy consumers, such as electrified railway systems. For instance, electric trains equipped with regenerative braking systems, as explored in reference [16], generate substantial energy that can be recovered and utilized if properly controlled. Forecasting energy consumption in these systems offers both economic and technical benefits, including the potential to harness regenerative braking energy. Additionally, power flow control systems, as investigated in reference [17], further underscore the importance of incorporating forecasting models to optimize energy distribution and enhance efficiency in electrified railways. This work emphasizes the important relationship between energy efficiency and the quality and quantity of information offered to customers. Short-term load forecasting is critical in enabling individuals to better manage their energy consumption. The main goal of this paper is to predict power usage using function fitting artificial neural networks and regression approaches. This study makes two significant contributions: (i) It clearly demonstrates the link between home energy efficiency and residents' access to information and technology, and (ii) it introduces a novel approach to short-term load forecasting that combines artificial neural networks with regression techniques to minimize mean squared error (MSE) over time.