Enhancing Electric Load Forecasting (ELF) in Smart Grids Using Neuro-Fuzzy Models: A Focus on Accuracy and Decision Process Transparency

In the domain of smart grid technologies, the capacity to forecast energy loads with high precision is crucial for the optimization of grid management and the promotion of sustainable energy practices. Traditional models for Energy Load Forecasting (ELF) often fail to fully comprehend the non-linear dependencies and unpredictable nature of energy consumption within these advanced systems. Addressing this gap, the current research introduces an innovative Neuro-Fuzzy approach, designed to enhance ELF within smart grid frameworks. This approach harnesses the predictive power of neural networks, renowned for their proficiency in learning from historical data, and combines it with the versatile reasoning capabilities of fuzzy logic systems, which excel in managing uncertainty and imprecision. By integrating these two computational paradigms, the proposed model exhibits a remarkable ability to navigate the complex and erratic patterns of energy usage, learning adaptively and accounting for the unpredictable variables that affect load demands. Empirical assessments conducted on datasets derived from actual smart grid implementations reveal the superior performance of the Neuro-Fuzzy model over contemporary forecasting methods. It not only delivers enhanced accuracy but also brings robustness to ELF. The model's intrinsic interpretability also plays a pivotal role in decision-making processes, allowing grid operators to transform intricate data analyses into practical, executable strategies.