A novel integrated price and load forecasting method in smart grid environment based on multi-level structure

Short-term load forecasting is an important part to support the planning and operation of power grid, but the current load forecasting methods have the problem of poor adaptive ability of model parameters, which are difficult to ensure the demand for efficient and accurate power grid load forecasting. To solve this problem, a short-term load forecasting method for smart grid is proposed based on multilayer network model. This method uses the integrated empirical mode decomposition (IEMD) method to realize the orderly and reliable load state data and provides high-quality data support for the prediction network model. The enhanced network inception module is used to adaptively adjust the parameters of the deep neural network (DNN) prediction model to improve the fitting and tracking ability of the prediction network. At the same time, the introduction of hybrid particle swarm optimization algorithm further enhances the dynamic optimization ability of deep reinforcement learning model parameters and can realize the accurate prediction of short-term load of smart grid. The simulation results show that the mean absolute percentage error e MAPE and root-mean-square error e RMSE of the performance indexes of the prediction model are 10.01% and 2.156 MW, respectively, showing excellent curve fitting ability and load forecasting ability.

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A Hybrid Load Forecasting Method Based on Neural Network in Smart Grid

10.1109/iccc52777.2021.9580230 ◽

2021 ◽

Author(s):

Jingyi Zhang ◽

Wenpeng Jing ◽

Zhaoming Lu ◽

Yueting Wang ◽

Xiangming Wen

Keyword(s):

Neural Network ◽

Smart Grid ◽

Load Forecasting ◽

Forecasting Method

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Power data mining in smart grid environment

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189355 ◽

2020 ◽

pp. 1-7

Author(s):

Ying Liu ◽

Guoshi Wang ◽

Wei Guo ◽

Yingbin Zhang ◽

Weiwei Dong ◽

...

Keyword(s):

Data Mining ◽

Smart Grid ◽

Smart Grids ◽

Load Forecasting ◽

Grid Environment ◽

Power Load ◽

Depth Analysis ◽

Power Load Forecasting ◽

Distribution Of Power ◽

National Industry

The power grid is the foundation of the development of the national industry. The rational and efficient distribution of power resources plays an important role in economic development. The smart grid is the use of modern network information technology to realize the exchange of data information between grid devices. The construction of smart grids has accumulated a huge amount of data resources. At present, the demand for power companies to “use data management enterprises and use the information to drive services” is increasingly urgent. Power big data has become the basis for grid companies to make decisions, but the accumulation of pure data does not bring benefits to grid companies. Therefore, making full use of these actual data based on the grid, in-depth analysis, and discovering and using the hidden information is of great significance for guiding the power companies to make correct decisions. This paper first analyzes the differences between smart grids and traditional grids and provides an overview of data mining techniques, including the association rules commonly used in association analysis. Then the application scenarios of data mining in the smart grid are put forward, and data mining technology is applied to power load forecasting. The experimental results show that the data mining method and actual results of the power load forecasting in the smart grid environment proposed in this paper are within a reasonable range. Therefore, the results of load forecasting in this paper are still of practical value.

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