USING ARTIFICIAL NEURAL NETWORKS FOR ELECTRIC LOAD FORECASTING

Аннотация

In today’s evolving energy landscape, accurate electric load forecasting has become critical for ensuring the stability and efficiency of power systems. Traditional methods often fall short due to increasing system complexity and variability. Therefore, this article explores the use of Artificial Neural Networks (ANNs) as a modern, adaptive solution. It highlights the strengths of ANNs in handling nonlinear data, integrating multiple input variables, and learning from past patterns. Moreover, it discusses challenges such as data requirements and interpretability. Ultimately, the article concludes that ANNs offer substantial benefits and will play a key role in the future of sustainable energy management. Keywords: artificial neural networks, electric load forecasting, energy systems, smart grids, machine learning, nonlinear modeling, energy management, demand prediction. As global energy demand continues to rise, the need for precise electric load forecasting has become more urgent than ever. Not only does accurate forecasting help ensure the reliability of power systems, but it also enables better economic planning, integration of renewables, and consumer satisfaction. Consequently, utility providers and grid operators are seeking more intelligent and flexible tools for predicting electricity consumption. Among these tools, Artificial Neural Networks (ANNs) have emerged as a promising solution due to their ability to model complex, nonlinear relationships. Thus, this article examines how ANNs are applied in electric load forecasting and why they offer advantages over traditional techniques. To begin with, electric load forecasting is essential for balancing supply and demand in real time, since electricity cannot be stored easily in large amounts. Furthermore, accurate forecasts allow energy providers to schedule generation efficiently, reduce reserve margins, and avoid over- or under-production. In addition, precise forecasting helps reduce operational costs and minimize environmental impact by avoiding unnecessary power plant usage. Moreover, with the increasing adoption of smart grids and renewable energy sources, forecasting becomes even more challenging and critical. As a result, more dynamic and intelligent models are needed—models that can adapt 110 DEVELOPMENT OF PEDAGOGICAL TECHNOLOGIES IN MODERN SCIENCES International scientific-online conference to changing conditions, variable inputs, and unexpected disruptions [1, 835- 846]. Historically, statistical models like linear regression, ARIMA, and exponential smoothing were widely used for load prediction. Although these models can perform reasonably well under stable conditions, they often assume linear relationships and stationary data, which limits their accuracy in more complex environments. For instance, such models may fail to account for sudden temperature spikes, holidays, or unusual consumption patterns. Therefore, while traditional approaches provide a basic framework, they are often insufficient in today’s dynamic energy systems [4, 442-449]. In contrast, Artificial Neural Networks offer a more robust approach. Not only can they capture nonlinear and dynamic relationships, but they also adapt over time through learning algorithms. Essentially, ANNs are composed of layers of interconnected neurons that process inputs and update weights based on prediction errors. As a result, they can identify patterns that are not visible through conventional techniques. Another key advantage is that ANNs can incorporate a wide range of input variables—such as temperature, humidity, time of day, and historical load— without needing to define their exact relationship in advance. Thus, they are especially well-suited for short-term and long-term load forecasting in complex systems [2, 124-132]. Artificial Neural Networks are increasingly being used in real-world scenarios. For example, in smart grid environments, ANNs are deployed to forecast load on an hourly or even real-time basis. Additionally, in demand response programs, these models help utilities adjust consumption patterns to reduce peak demand. Moreover, electric vehicle charging networks benefit from accurate forecasting to manage energy usage efficiently and prevent grid overload. In some cases, hybrid models that combine ANNs with fuzzy logic, genetic algorithms, or statistical tools offer even greater predictive power. Such integration allows for handling both precise data and uncertain or qualitative inputs, making the models even more versatile. Despite their many advantages, ANN-based models are not without challenges. For instance, training deep networks requires significant computational power, especially with large datasets. Moreover, overfitting can occur if the model becomes too tailored to the training data and loses generalization. Another concern is the interpretability of neural networks— 111 DEVELOPMENT OF PEDAGOGICAL TECHNOLOGIES IN MODERN SCIENCES International scientific-online