A Statistical Data-Filtering Method Proposed for Short-Term Load Forecasting Models
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ORIGINAL ARTICLE
A Statistical Data‑Filtering Method Proposed for Short‑Term Load Forecasting Models Duong Minh Bui1 · Phuc Duy Le4 · Tien Minh Cao4 · Hung Nguyen4 · Trang Thi Pham2 · Duy Anh Pham3 Received: 15 November 2019 / Revised: 5 May 2020 / Accepted: 18 May 2020 © The Korean Institute of Electrical Engineers 2020
Abstract Reliability assessment of the SCADA-system based load data is necessary for improving accuracy of short-term load forecasting (STLF) methods in a distribution network (DN). Specifically, the reliability evaluation of the load data is to properly eliminate noise/outliers caused by random power consumption behaviors or the sudden change in load demand from industrial and residential customers in the DN. Thus, this paper proposes a novel statistical data-filtering method, working at an input data pre-processing stage, which will evaluate the reliability of input load data by analyzing all possible data confidence levels in order to filter-out the noise/outliers for accuracy improvement of different short-term load forecasting models. The proposed statistical data-filtering method is also compared to other existing data-filtering methods (such as Kalman Filter, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA)). Moreover, several case studies of short-term load forecasting for a typical 22 kV distribution network in Vietnam are conducted with an Artificial Neural Network (ANN) model, a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model, a combined model of Long Short-Term Memory Network and Convolutional Neural Network (LSTM-CNN), and a conventional Autoregressive Integrated Moving Average (ARIMA) model to validate the statistical data-filtering method proposed. The achieved results demonstrate which the STLF using ANN, LSTM-RNN, LSTMCNN, and ARIMA models with the statistical data-filtering method can all outperform those with the existing data-filtering methods. Additionally, the numerical results also indicate that in case the SCADA-based load data is normally distributed, time-series forecasting models should be more preferred than neural network models; otherwise, when the SCADA-based load data contains multiple normally distributed sub-datasets, neural network-based prediction models are highly recommended. Keywords ARIMA · Confidence level · Data filtering · Neural network · Long short-term memory · And short-term load forecasting
1 Introduction Short-term load forecasting (STLF) plays an increasingly significant role in design, operation, and planning of distribution networks. However, the diversity of load
profiles could lead to certain difficulties in implementation of short-term load forecasting. One of the difficulties is that the electricity demand of industrial customers is much higher than that of residential customers such that the residential load profiles have been inappropriately 1
Phuc Duy Le [email protected]
Department of Electrical and Computer Engineering
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