Short-Term Electric Load Forecasting Based On Optimized Artificial Bee Colony Algorithm

After the introduction of the Energy Internet,the energy issue has received widespread attention.Electrical energy has also received due attention,and short-term power load forecasting is both a key and a difficult point in the power industry.The power sector conducts production and scheduling based on the power load forecast data.The accuracy of the forecast data directly affects the amount of raw materials used and the reasonable distribution of power and energy.Therefore,it is a key issue in the power industry.Because there are many factors affecting load forecasting,and each factor depends on each other,the difficulty of load forecasting is increased.Therefore,short-term power load forecasting has important practical significance.Based on the characteristics of short-term electric load,this paper analyzes various influencing factors,and uses the artificial artificial bee colony algorithm optimized by simulated annealing algorithm to predict and design and develop the electric load forecasting system.The main contents are as follows.First,research and analysis of various factors affecting power load forecasting,including day type,weather conditions,and selection of historical data.Based on these influencing factors,the influence attribute is selected by experience,and the attribute reduction algorithm based on rough set is used to reduce it,delete the redundant noise attribute,use the key attribute as the input vector of the model,and verify with BP network model.The validity of attribute reduction.Secondly,by comparing the group intelligence algorithm,the artificial bee colony algorithm is selected for load forecasting.In the modeling process,the artificial bee colony algorithm is found to have slow iteration speed and easy to fall into local minimum value.Then the artificial bee colony algorithm is optimized by simulated annealing algorithm,and the network prediction model based on ABC-SA algorithm is established.The parallel search structure of artificial bee colony algorithm(ABC)and the probability jump feature of simulated annealing algorithm(SA)are combined.Conduct power load forecasting.The prediction results before and after optimization are compared,and the optimized model prediction results are more accurate.On this basis,the selection of historical data is adjusted,and part of the data that has been generated on the day of the forecast is regarded as the new interest data,which is applied to the load forecasting.The experimental comparison shows that the data using the new interest data for prediction is more accurate.Finally,in order to timely and effectively present the results of load forecasting to power production and dispatching departments,the power load forecasting system is designed and developed according to actual needs.The main functions of the system include load forecasting,data management,data preprocessing,and load.Analysis and access control,etc.These functions are interrelated and independent of each other and can basically meet the needs of the power system.