# Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model
## 1 Introduction  
As quality of life has improved, the demand for power energy has also risen, and thus, the requirements for power generation and transmission and the use of electricity have increased [1]. Power load forecasting applies machine learning methods to mine the key factors affecting load from historical data, such as weather and time data, to build load forecasting models [2]. Load forecasting is the basis for ensuring the balance of power supply and demand. Accurate forecasting results can reduce the pressure on transmission and distribution links and facilitate the optimal scheduling of power transmission links. They can effectively reduce power generation costs and improve economic and social benefits [3; 4]. Traditional forecasting methods use the overall historical load numbers as their basis [5; 6; 7; 8; 9]. The trend extrapolation predicts future loads based on the historical trends of the predictor variables. It predicts near future loads with high accuracy, but error gradually increases with time. The regression analysis method predicts future load changes by establishing a functional relationship between variables. It has high fitting ability but has a large error in predicting future changes. Time series extrapolation (the autoregressivemoving average model) approximates a nonlinear relationship with a linear equation [10]. The grey forecasting method performs correlation analysis by identifying the degree of dissimilarity in development trends between system factors. Traditional forecasting models have a relatively simple structure and require highly accurate historical data, resulting in forecasting results with poor accuracy. In recent decades, deep learning has made important breakthroughs in the field of artificial intelligence. In 1957, [PERSON] invented an artificial neural network to solve complex problems by means of a multilayer perceptron (MLP). Deep neural networks (DNNs) are currently a popular topic in load prediction research, and recurrent neural networks (RNNs) have advantages in memory parameter sharing and Turing completeness [11]. Long short-term memory (LSTM) networks overcome the problem that RNNs are prone to gradient disappearance/explosion, but their ability to correct errors is weak [12]. [PERSON] proposed a BLSTM network that can make full use of the hidden layer history state and has stronger robustness [13]. Yet, BLSTM networks have many parameters, and without proper optimization, the model may be overfitted or slow to train, resulting in inefficiency. The number of hidden layer neurons, the number of training epochs, the learning rate, and the regularization coefficient in the BLSTM network are optimized using the IVIA. It can improve the model's efficiency. The challenges of the machine learning algorithms to load forecasting for power systems, with complex, nonsmooth, nonlinear time-series and \"noisy\" data, will decrease the accuracy of the algorithm, and a single prediction model has difficulty meeting the accuracy requirements. Integrating a hybrid preprocessing method can significantly improve the prediction accuracy [14; 15; 16; 17]. In summary, to improve the accuracy and meet the needs of practical problems, this paper proposes a new model based on data mining and deep learning. It considers not only historical load data but also weather information, date types, real-time electricity prices, etc. Historical data load, weather information, and real-time electricity prices are normalized, and the date and holiday information are expressed through One-Hot encoding. The DBFCM algorithm is applied to load curve clustering to overcome the problems of traditional C-means clustering and fuzzy C-means clustering in which data are clustered into small classes, inaccurate classification is performed, and the \"uniform effect of the cluster size\" degrades performance. First, the LVMD is used to decompose and denoise the input data to improve the continuity and stability of the data. Second, the DBFCM algorithm is used to perform load curve clustering.