Abstract
Load forecasting (LF) is essential in enabling modern power systems’ safety and economical transportation and energy management systems. The dynamic balance between power generation and load in the optimization of power systems is receiving increasing attention. The intellectual development of information in the power industry and the data acquisition system of the smart grid provides a vast data source for pessimistic load forecasting, and it is of great significance in mining the information behind power data. An accurate short-term load forecasting can guarantee a system’s safe and reliable operation, improve the utilization rate of power generation, and avoid the waste of power resources. In this paper, the load forecasting model by applying a fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network (ILSTM-NN), and then establish short-term load forecasting using this novel model. Sparrow Search Algorithm is a novel swarm intelligence optimization algorithm that simulates sparrow foraging predatory behavior. It is used to optimize the parameters (such as weight, bias, etc.) of the ILSTM-NN. The results of the actual examples are used to prove the accuracy of load forecasting. It can improve (decrease) the MAPE by about 20% to 50% and RMSE by about 44.1% to 52.1%. Its ability to improve load forecasting error values is tremendous, so it is very suitable for promoting a domestic power system.
Highlights
Power systems are vital to people’s lives, business activities, industrial development, and scientific research, and can contribute significantly to the development of socioeconomic science
Another example is Che et al, who proposed a support vector regression model based on different “kernels”, using different kernel functions to establish a single-kernel support vector regression model and combined them according to the inverse ratio of errors
They constructed four different four-layer deep neural network forecasting models, validated and selected super-optimal parameters, and using the primary component regression method based on sliding windows, built a constructive load forecasting model
Summary
Power systems are vital to people’s lives, business activities, industrial development, and scientific research, and can contribute significantly to the development of socioeconomic science. For example, propose a combination model that combines neural networks with Fuzzy Expert Systems (FES) for short-term load forecasting. An adaptive fuzzy correction scheme obtains the final load forecasting Another example is Che et al, who proposed a support vector regression model based on different “kernels”, using different kernel functions to establish a single-kernel support vector regression model and combined them according to the inverse ratio of errors. Jihoon et al sought to overcome the single layer of a deep neural network optimal, super parametric, hidden number, which made it complicated to determine the problem based on the training set They constructed four different four-layer deep neural network forecasting models, validated and selected super-optimal parameters, and using the primary component regression method based on sliding windows, built a constructive load forecasting model. We can obtain more accurate load forecasting when dealing with the load under the influence of various factors (such as temperature, humidity, rainfall, holidays, etc.) and consider the individual factors
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