International Core Journal of Engineering 2020-26 | Page 166

2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) Distribution Network Load Forecasting Based on Improved BP Neural Network Zheng Gong Zhenghua Zhang* MengSheng Wu Hang Ni School of Information Engineering Yangzhou University Yangzhou, China [email protected] School of Information Engineering Yangzhou University Yangzhou, China [email protected]* State Grid Yangzhou Power Supply Company Yangzhou, China Yangzhou Xinyang Switchgear Co., Ltd. Yangzhou, China II. D ISTRIBUTION N ETWORK L OAD Abstract—In order to overcome the inherent defects of traditional neural networks, and further improve the short- term power load prediction accuracy of the station, the genetic algorithm is introduced to optimize the classical neural network algorithm, the initial value and threshold of the neural network are recalculated, and the prediction model is established. The predicted results are obtained through simulation training and compared. Finally, the effectiveness of the algorithm is proved by an example of a city. The load is usually divided into four types: industrial power load, agricultural power load, transportation power load, municipal life and lighting power. The municipal life and lighting power that is closer to us include utilities, residents, businesses, schools, institutions, and troops. Among them, the utility load is relatively stable, and the residential and commercial power load is affected by the season, and it is extremely obvious. Therefore, in general, this type of power load fluctuates greatly, sometimes directly affecting the seasonal variation of the peak load of the distribution network. Keywords—Power system; genetic algorithm; neural network; short-term load forecasting; I. I NTRODUCTION The load characteristics of the distribution network reflect the inherent nature and variation of the distribution network load, such as amplitude, power factor, fluctuation, seasonal variation, concentration, and environmental impact. Studying and mastering the load characteristics of the distribution network is conducive to rationally configuring the capacity and form of the equipment, and can continuously improve the economic benefits of the distribution network and the safety and reliability of the power supply. With the further development of modern technology, 澳 based on the physical grid, the smart grid [2] , which is formed by the integration of advanced sensing technology [1] , communication technology, information technology, computer technology and control technology with the physical grid, has entered people's lives. 澳 At the same time, the requirements for smart grids are also increasing. One of them is that the power consumption mode can be adjusted according to the fluctuation of real-time electricity price, which requires accurate monitoring of the load of the power grid [3] and pre-judgment [4] . 澳 The magnitude of the load is directly related to the stability of the grid, so effective monitoring of the load is necessary. By prediction of the power and electricity consumption of the load in a certain period of time in the future, not only can 澳 provide the data needed for the development of the work plan and development plan for the distribution network operation department and the planning and design department but also through the smart home [6] , intelligent control equipment and other terminals, use the applications installed on the mobile phone can remotely control water heaters, air conditioners, refrigerators and other electrical appliances [7] , to achieve the Internet of Things. 澳 This is not only in line with the needs of the ‘Internet +’, but also has great practical significance for ensuring stable and efficient operation of the power grid, ensuring smooth social production security, and energy- saving and emission-reduction peak power [8] . A. Load Forecasting According to the forecast period, it can be divided into short-term, near-term (5-year), medium-term (10-year), and long-term (20-year) forecasts. The predictions taken in this paper are based on short-term load forecasting [14] . Short-term forecasting is generally used for the dispatching operation of the distribution network, with the predicted power as the main, in order to formulate the power production plan, determine the power distribution operation mode and arrange equipment maintenance and replacement procedures. 澳 According to the needs of short-term load forecasting, continuous forecasting can be made on a monthly, weekly, daily, hourly, or even minute basis during the year. At present, the more conventional forecasting method mainly includes four steps: 澳 澳 1) Determine the amount (power or electricity consumption) to be predicted and the time interval between the forecast quantities, and ask for enough historical data. This paper proposes to optimize on the basis of neural network [10] ,introduce genetic algorithm [11] to get better network initial weight and threshold [12] , improve the accuracy of load forecasting [13] , through the urban power load The prediction contrast proves the superiority of the algorithm. 978-1-7281-4691-1/19/$31.00 ©2019 IEEE DOI 10.1109/AIAM48774.2019.00036 2) Analyze the historical data of the load. Establish a model that is applied to the process of surface ripe load change, and identify it and estimate the parameters roughly. 3) Perform accurate estimation of parameters and scientific testing of models. 144