Load Forecasting Based On Elman Neural Network Optimized By Beetle Antennae Search Optimization

Power system load forecasting is the basic content of power system operation management and real-time control. The accuracy of prediction results is of great significance to the security, optimization and economic operation of power system. Elman neural network has a lot of research in data prediction, in which the method of calculating weight and threshold is gradient descent method. In this paper, beetle antennae search optimization is used to find the weight and threshold of Elman neural network, and apply it to the set network, so as to construct the final training model. The model constructed by this method can overcome the problems of poor stability and easy to fall into local optimization of standard Elman neural network. According to the load data of each hour of the power company from 2008 to 2009, as well as the daily temperature, holiday type and other data, the load of 12:00 and 6 pm every day in January 2010 is predicted. It is verified that the accuracy of this model is higher than that of Elman neural network. The experimental results proved that the algorithm is competitive.

[1]  R. Weron.  Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach , 2006 .

[2]  P. Suganthan,et al.  A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem , 2011 .

[3]  Shengyao Wang,et al.  An effective artificial bee colony algorithm for the flexible job-shop scheduling problem , 2012 .

[4]  Ignacio E. Grossmann,et al.  Challenges in the application of mathematical programming in the enterprise-wide optimization of process industries , 2014, Theoretical Foundations of Chemical Engineering.

[5]  Quan-Ke Pan,et al.  Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm , 2015, Inf. Sci..

[6]  Ruiming Fang,et al.  The ultra-short term power prediction of wind farm considering operational condition of wind turbines , 2016 .

[7]  Quan-Ke Pan,et al.  An Improved Artificial Bee Colony Algorithm for Solving Hybrid Flexible Flowshop With Dynamic Operation Skipping , 2016, IEEE Transactions on Cybernetics.

[8]  Mark Last,et al.  Short-Term Load Forecasting in Smart Meters with Sliding Window-Based ARIMA Algorithms , 2017, ACIIDS.

[9]  Shuai Li,et al.  BAS: Beetle Antennae Search Algorithm for Optimization Problems , 2017, ArXiv.

[10]  Mark Last,et al.  Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms , 2017, Vietnam Journal of Computer Science.

[11]  Jinde Cao,et al.  A Hybrid Pareto-Based Tabu Search for the Distributed Flexible Job Shop Scheduling Problem With E/T Criteria , 2018, IEEE Access.

[12]  Yanfei Li,et al.  Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network , 2018 .

[13]  Zhigang Zeng,et al.  A modified Elman neural network with a new learning rate scheme , 2018, Neurocomputing.

[14]  David J. Hill,et al.  Short-Term Residential Load Forecasting Based on Resident Behaviour Learning , 2018, IEEE Transactions on Power Systems.

[15]  Hong Liu,et al.  A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm , 2018, Appl. Soft Comput..

[16]  Junqing Li,et al.  An Efficient Optimization Algorithm for Resource-Constrained Steelmaking Scheduling Problems , 2018, IEEE Access.

[17]  Fangfang Li,et al.  A new beetle antennae search algorithm for multi-objective energy management in microgrid , 2018, 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[18]  Peiyong Duan,et al.  An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system , 2019, Int. J. Prod. Res..

[19]  Zhijing Yang,et al.  Conditioning optimization of extreme learning machine by multitask beetle antennae swarm algorithm , 2018, Memetic Comput..