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

• Published in: IEEE access Vol. 9; pp. 50344 - 50357
• Main Authors: Shang, Chuan, Gao, Junwei, Liu, Huabo, Liu, Fuzheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Clustering; Clustering algorithms; CNN; Convolution; Correlation coefficients; cosine similarity; Data mining; Data models; Economic models; Forecasting; Kernel; Load forecasting; Load modeling; LSTM; Machine learning; Mathematical models; Neural networks; Particle swarm optimization; Pearson correlation coefficient; Prediction algorithms; Predictive models; PSO-KFCM; Short-term load forecasting; Similarity
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3067043

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.