Very Short Term Load Forecasting Using Cartesian Genetic Programming Evolved Recurrent Neural Networks (CGPRNN)

Forecasting the electrical load requirements is an important research objective for maintaining a balance between the demand and generation of electricity. This paper utilizes a neuro-evolutionary technique known as Cartesian Genetic Programming evolved Recurrent Neural Network (CGPRNN) to develop a load forecasting model for very short term of half an hour. The network is trained using historical data of one month on half hourly basis to predict the next half hour load based on the 12 and 24 hours data history. The results demonstrate that CGPRNN is superior to other networks in very short term load forecasting in terms of its accuracy achieving 99.57 percent. The model was developed and evaluated on the data collected from the UK Grid station.