Hybrid time series-bayesian neural network short-term load forecasting with a new input selection method

Abstract

This paper proposes a hybrid short-term load forecasting (STLF) framework with a new, more efficient, input selection method. Correlation analysis and l2-norm are used in combination to select suitable inputs to individual Bayesian neural networks (BNNs), which are used to forecast the load. Forecast outputs are then weighted using calculated weighting coefficients and summed to obtain the final forecast for a particular day. New England load data is used to assess the accuracy and performance of the proposed framework; furthermore, a comparison of the proposed STLF with classic time-series methods shows a significant improvement in the accuracy of the load forecast.