Abstract
This paper proposes a load forecasting method based on LSTM model, fully explores the regularity of historical load data of industrial park enterprises, inputs the data features into LSTM units for feature extraction, and applies the attention-based model for load forecasting. The experiments show that the accuracy of our prediction model and early warning model is better than that of the baseline and can reach the standard of application in practice; this model can also be used for early warning of local sudden large loads and identification of enterprise power demand. Therefore, the validity of the method proposed in this paper is verified using the historical dataset of industrial parks, and relevant technical products and business models are formed to provide value-added services to users by combining existing practical cases for the specific scenario of industrial parks.
Highlights
Power load forecasting is an important problem in power field
With the development of power market, accurate short-term forecasting of power load can effectively guarantee the safe operation of power grid, reduce the cost of power generation, meet the needs of users, and improve social and economic benefits [2]
Short-term power load forecasting is to forecast the power load in a short period of time in the future according to the power load in the past and load related data such as temperature, humidity, and date type
Summary
Power load forecasting is an important problem in power field. Accurate load forecasting of power system is the basis of efficient management, which provides support for the operation and scheduling of power enterprise [1]. With the development of power market, accurate short-term forecasting of power load can effectively guarantee the safe operation of power grid, reduce the cost of power generation, meet the needs of users, and improve social and economic benefits [2]. Accurate prediction is conducive to timely macro control of users’ electricity consumption behavior and to provide scientific guidance for power production [4]. Load forecasting based on intelligent forecasting algorithm is widely used in the field of power load forecasting because of its high stability and forecasting accuracy, strong complex mapping, fault tolerance, and generalization ability [5]
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