Short-Term Load Forecasting Based on Deep Learning for End-User Transformer Subject to Volatile Electric Heating Loads

Qifang Chen,Mingchao Xia,Xichen Jiang,Qinfei Sun,Wenxia Liu,Teng Lu

doi:10.1109/access.2019.2949726

Abstract

Short-Term Load Forecasting (STLF) for End-User Transformer Level (EUTL) is challenging due to the high penetration of Electric Heating Loads (EHLs), which exhibit significant uncertainty, nonlinearity, and variability. In this paper, a STLF model is proposed based on the Stacked Auto-Encoder Extreme Learning Machine (SAE-ELM) deep learning framework, which can be used to extract hidden features from the time series load data. In order to improve the capability of extracting deep and diverse features from the data and generate a useful knowledge representation structure, a novel specialized feature indices set is proposed to construct the training sample set. The sliding trend, fluctuation rate, grade of change, and smoothness of the time series are considered and quantified as elements of the training sample set. Then, deep nonlinear features are extracted by using the SAE-ELM with no iterative parameter tuning needed. To illustrate the validity of the proposed model, five numerical cases are conducted. Comparison of results shows that the proposed model improves the capability and sensitivity of dealing with load volatility and forecasting accuracy.

Highlights

Short-Term Load Forecasting (STLF) is essential for maintaining the balance between supply and demand in a power system [1], [2]
(2) Due to the better deep feature extracting and learning ability, the STLF models based on deep learning framework (SAE-ELM and long short-term memory (LSTM)) generally performs better than other conventional methods
In this paper, a STLF model at End-User Transformer-Level (EUTL) with high penetration of Electric Heating Loads (EHLs) is proposed based on a novel specialized feature indices set and Stacked Auto-Encoder Extreme Learning Machine (SAE-ELM) deep learning framework

Summary

Introduction

Short-Term Load Forecasting (STLF) is essential for maintaining the balance between supply and demand in a power system [1], [2]. In order to forecast the load of the (t + 1)th time interval, all the attribute series at the tth time interval in all previous days are input into SAE-ELM in parallel, which is shown as step 1 in Fig. 6 and described by (20). The attribute set in (20) is taken as input to train deep learning neural network based on SAE-ELM to determine connecting weights in UDFE and FM process according to (9) - (19).

Results

Conclusion