Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features

Abstract

Accurate short-term load forecasting (STLF) is essential for power grid systems to ensure reliability, security and cost efficiency. Thanks to advanced smart sensor technologies, time-series data related to power load can be captured for STLF. Recent research shows that deep neural networks (DNNs) are capable of achieving accurate STLP since they are effective in predicting nonlinear and complicated time-series data. To perform STLP, existing DNNs use time-varying dynamics of either past load consumption or past power correlated features such as weather, meteorology or date. However, the existing DNN approaches do not use the time-invariant features of users, such as building spaces, ages, isolation material, number of building floors or building purposes, to enhance STLF. In fact, those time-invariant features are correlated to user load consumption. Integrating time-invariant features enhances STLF. In this paper, a fuzzy clustering-based DNN is proposed by using both time-varying and time-invariant features to perform STLF. The fuzzy clustering first groups users with similar time-invariant behaviours. DNN models are then developed using past time-varying features. Since the time-invariant features have already been learned by the fuzzy clustering, the DNN model does not need to learn the time-invariant features; therefore, a simpler DNN model can be generated. In addition, the DNN model only learns the time-varying features of users in the same cluster; a more effective learning can be performed by the DNN and more accurate predictions can be achieved. The performance of the proposed fuzzy clustering-based DNN is evaluated by performing STLF, where both time-varying features and time-invariant features are included. Experimental results show that the proposed fuzzy clustering-based DNN outperforms the commonly used long short-term memory networks and convolution neural networks.

Short-Term Electric Load Forecasting Based on Signal Decomposition and Improved TCN Algorithm

“…The high-frequency component additionally includes optimization for the soft threshold. The dilations and epochs are not subject to optimization and are set at [1,2,4,8] and 100, respectively. The population size (pop) of the SMA optimization algorithm is set to 15, with a maximum iteration number (MaxIter) of 10.…”

“…The escalating demand for electricity in contemporary society, along with the increasing complexity of power load variations, has heightened requirements for the reliability, economic efficiency, and sustainability of our electricity supply [1]. As a vital component of the electric power industry, load forecasting is categorized into short-term, medium-term, and long-term forecasts, depending on the forecast horizon [2].…”