Application of land spatial information perception technology in power system load forecasting

CHEN Zhiyuan; YANG Xuan; LI Ling

doi:10.7688/j.issn.1000-1646.2025.03.01

Abstract：[Objective]To address the problems of traditional load forecasting methods,such as low information utilization efficiency,large errors,and difficulties in adapting to the diversity and randomness of actual power load changes,a power system load forecasting method based on land spatial information perception was proposed.This method could improve the accuracy and reliability of load forecasting,provide key data support for power system planning and construction,and meet the needs of economic and social development.[Methods]This algorithm adopted the strategy of classification by area with the use of urban land spatial information and power grid load data.For developed areas(with loads known),historical load data were used for curve fitting to carry out load forecasting.As for newly developed areas(with loads unknown),the average load density of the same land type in developed areas was used for equivalentprocessing to form the basic information of load forecasting,and then load forecasting was carried out.At the same time,this algorithm subdivided the historical load data and equivalent load data.The algorithm integratedthe exponential model,the growth curve model,and the elastic coefficient model and mainly used the combination of dynamic weights to form the best fitting scheme.Using the above methods,a power system load forecasting technology based on land spatial information perception was formed.The historical data from 2014 to 2020 were used as the benchmark for parameter fitting,and the historical load data of industrial power,residential power,commercial power,public facilities power,and other power types were sorted out.The load data of 2021 were taken as the forecasting object.The differences of the exponential model,the growth curve model,and the elastic coefficient model in total forecast and forecast based on classification by area were compared and analyzed through experiments.The results show that the forecasting accuracy based on classification by area is about 33％higher than that of total forecasting.On this basis,the best fitting effects based on dynamic weights and mean weights were compared and analyzed.The calculation results show that the best fitting forecasting error is only 1.12％when dynamic weights are used,which is 12％smaller than the forecasting error in the case of using mean weights.In conclusion,the scheme proposed can significantly improve the accuracy and reliability of load forecasting.[Results]The results of this paper show that the accuracy of the load forecasting model can be improved by using the method of classification by area to classify spatial information according to the land and load types.The algorithm has better dynamic adaptability by dynamically adjusting parameter weights and integrating single forecasting models,able to achieve the best fitting results and enhance forecasting accuracy.[Conclusion]The highlights of this paper are as follows.Firstly,the data processing method of classification by area is adopted to improve the utilization rate of spatial information and load information of the urban power grid.Secondly,the traditional load forecasting models are integrated by using dynamic weights,which breaks the limitations of single models.This algorithm further improves the accuracy and reliability of urban power grid load forecasting through the above two approaches.