Short-Term Load Forecasting Based on PSO-KFCM Daily Load Curve Clustering and CNN-LSTM Model

Chuan Shang,Huabo Liu,Fuzheng Liu,Junwei Gao

doi:10.1109/access.2021.3067043

Chuan Shang, Huabo Liu + Show 2 more

Open Access

https://doi.org/10.1109/access.2021.3067043

Copy DOI

Abstract

Short-term load forecasting (STLF) with excellent precision and prominent efficiency plays a significant role in the stable operation of power grid and the improvement of economic benefits. In this paper, a novel model based on data mining and deep learning is proposed. Firstly, the preprocessing of data includes normalization of historical load, and fuzzification of influencing factors (meteorological factors, date types and economy) based on Pearson correlation coefficient (PCC). Secondly, kernel fuzzy c-means (KFCM) modified by particle swarm optimization (PSO-KFCM) algorithm clusters the daily load curve. In the clustering experiments, the within-cluster sum of squared error (SSE) index is presented to determine the number of clusters and the clustering validity has a 31.9% enhancement compared with the traditional FCM algorithm. Thirdly, the cosine similarity establishes the resemblance between the prediction date and each cluster, and the similar cluster is determined according to the principle of maximum similarity. Finally, a multivariate and multi-step hybrid model MMCNN-LSTM based on convolution neural network (CNN) and long short-term memory (LSTM) neural network is proposed to forecast the load in following 24 hours, in which similar cluster data is applied to training set. To demonstrate the effectiveness of proposed integrated technique, the accuracy has been verified in three predictive experiments. The fruitful results indicated that the average mean absolute percent error (MAPE) in the entire test set was only 1.34%, a 3.02% reduction compared to a single LSTM.

Highlights

Power load forecasting is to forecast the future load data with historical data as the key component [1]
Power load forecasting can be divided into long-term load forecasting (LTLF), medium-term load forecasting (MTLF), short-term load forecasting (STLF) and very short-term load forecasting (VSTLF) according to the forecast duration
Them, STLF refers to the prediction of the future daily load or weekly load, which is mainly worked for power system operation dispatching, guaranteeing the safety of power grid process and improving the operational efficiency

Summary

INTRODUCTION

Power load forecasting is to forecast the future load data with historical data as the key component [1]. THE PROPOSED METHOD The proposed short-term load forecasting based on PSO-KFCM algorithm and CNN-LSTM model is shown in Fig.. For the purpose of settling the problem of poor clustering quality caused by the constraint condition, the kernel function is introduced into KFCM algorithm, which maps the points of the original space to the high-dimensional feature space. I=1 j=1 where represents a nonlinear mapping, the Euclidean distance xj − ci 2 in the traditional FCM algorithm is rewritten as (xj) − (ci) 2, (xj) and (ci) are the images of sample data and clustering center mapped from the original space to the high-dimensional feature space respectively. The concrete steps of KFCM algorithm are as follows: 1) Initialize the maximum number of iteration steps M , number of clusters k, fuzzy weighting coefficient m, RBF kernel parameters σ , termination threshold of the objective function δ and iteration step l = 0.

THE PRINCIPLE OF THE COSINE SIMILARITY

Findings

THE PROMCIPLE OF THE CNN-LSTM MODEL