An improved self-organizing incremental neural network model for short-term time-series load prediction

Abstract

Load prediction is a crucial component for optimal building energy management. The challenge with buildings’ load prediction is the lack of historical data since not all sites have a large amount of collected data. One of the solutions is incremental learning that updates the trained model with the most recent data. It allows the model to be deployed as soon as possible and improving its prediction accuracy as time progress. This study studies the effect of incremental learning. This study also proposes a novel DB-SOINN-R model that is based on the enhanced self-organizing incremental neural network (ESOINN), which is one of the incremental learning models. The problems with ESOINN are the inappropriate node removal by the original denoising of ESOINN, the inappropriate Euclidean distance for training data with imbalanced dimensions that usually consist of less discrete timestamp data compared to time-series historical data, and the incapability to obtain unique predicted outputs. To tackle these problems, the DB-SOINN-R incorporates a new density-based denoising that replaces the original denoising, a new mean Euclidean distance as the distance metric to handle training data with imbalanced dimensions, and k-nearest-neighbor inverse distance weighting (kNN-IDW) regression to obtain unique predicted output for every different input. The proposed DB-SOINN-R is compared with five models: feedforward neural network, deep neural network with long-short-term memory, support vector regression, ESOINN, and kNN regression. They are tested on day-ahead and one-hour-ahead load predictions, using two different datasets. The proposed DB-SOINN-R has the highest prediction accuracy among all models with incremental learning in both datasets.