Short term load forecasting by artificial neural network

Abstract

Electrical power load forecasting has at all times been an major issue in energy trade. Load forecasting is generally made through developing models on relative knowledge, reminiscent of local weather and previous load demand knowledge. Such forecast is almost always aimed towards brief-time period prediction like one-day forward prediction, on the ground that longer interval prediction (mid-term or long term) will not be reliant as a result of error propagation. The accurateness of load predicting needs a massive effect on an electricity services process and making cost. Exact load predicting is hence significant, particularly with the fluctuation shappening within the utility industry because of deregulation and competition. Several outmoded approaches such as regression model, time series model and expert system have been proposed for short term load forecasting by different degree of achievement. Artificial Neural Network established short term load forecasting model has its own importance due to its transparent model, easy implementation, and superior performance. In this paper ANN trained through back propagation in combination with Genetic Algorithm model is used aimed at short term load forecasting. In back propagation, the weights of neuron changes according to the gradient descent which may tend to local minima, so Genetic Algorithm is implemented which gives better forecasting result as compared to back propagation.

Cite this paper

@article{Ray2016ShortTL, title={Short term load forecasting by artificial neural network}, author={Papia Ray and Debani Mishra and Rajesh Kumar Lenka}, journal={2016 International Conference on Next Generation Intelligent Systems (ICNGIS)}, year={2016}, pages={1-6} }