Research on Electric Load Forecasting Model Using Elman Neural Network in MATLAB

In this research, we utilize MATLAB to develop an electric load forecasting model based on the Elman Neural Network. This model aims to predict future electricity demand by analyzing and learning from historical data patterns. The Elman Neural Network, a type of recurrent neural network (RNN), features context memory units that effectively handle temporal dependencies in time-series data. Through iterative training and parameter optimization of electrical load datasets, we construct an accurate and reliable forecasting system that assists power industry stakeholders in making informed decisions and strategic planning.

Our implementation involves key MATLAB functions such as newelm for creating the Elman network architecture, with customized configurations for input delays and hidden layer neurons. The training process employs backpropagation through time (BPTT) algorithm with Levenberg-Marquardt optimization (trainlm) for efficient weight adjustment. Data preprocessing steps include normalization using mapminmax and time-series windowing to format sequential inputs. Through this study, we aim to provide deeper insights into electric load forecasting methodologies while delivering practical solutions for the power sector. We believe the integration of MATLAB's computational capabilities with Elman Network's temporal processing strengths can drive innovations in load prediction accuracy.

In summary, this research presents a MATLAB-implemented Elman Neural Network model for electric load forecasting, utilizing historical data analysis to predict future demand patterns. The model's architecture incorporates recurrent connections that maintain internal states, enabling capture of seasonal trends and consumption cycles. Experimental results demonstrate how appropriate activation functions (tansig/purelin) and training epochs configuration impact prediction performance. We anticipate this work will contribute to improved power system stability and operational efficiency, while establishing new directions for intelligent grid management through machine learning applications.