Research and Source Code of Load Forecasting Based on Elman Neural Network
With the development of society, the power system is becoming more and more important in the modernization construction, and load forecasting is one of the core issues in the operation and management of the power system, and its accuracy has become a prerequisite for ensuring the safe and stable operation of the power grid. Traditional load forecasting methods often require a large amount of historical data and statistical analysis, and their accuracy is not high. In recent years, load forecasting methods based on artificial intelligence technology have gradually emerged.
Elman neural network is a commonly used artificial neural network model, which has the characteristics of feedback connection, can model sequence data, and is widely used in prediction problems in various fields. This article will introduce how to use Elman neural network for power load forecasting, and give the corresponding source code implementation.
First, we need to prepare the dataset. This paper uses the historical power load data set of a certain city in China, which contains load data of 365 days and load values of 288 time points per day. For convenience, we arrange the data sets in chronological order, and use the data storage format .mat that comes with Matlab to facilitate subsequent reading.
Next, we need to preprocess the data. Since the Elman neural network needs to input a specific format, we first need to divide the data into time windows, and each time window contains the load values of t consecutive time points. In this article, we set the time window size to 24, that is, one day's load data as a time window. At the same time, we also need to normalize the data and scale the data to the [0,1] range for better training.
Next, we can start to build the Elman neural network model. In this article, we use the neural network toolbox that comes with Matlab for modeling. Specifically, we need to set up the input layer, hidden layer and output layer of the neural network, where the hidden layer uses Elman feedback connection. In this article, we set the number of nodes in the hidden layer to 10. Regarding how to set the parameters of the neural network, we can optimize it through trial and error, and this article will not elaborate.
After building the neural network, we need to train it. In this article, we