Improved coyote algorithm and application to optimal load forecasting model

Abstract

Accurate load forecasting is critical to guarantee the security, steadiness and economic operation of the power system. Therefore, in order to improve the load forecasting accuracy, improved coyote optimization (ICOA) was proposed in this paper to optimizing the Extreme learning machine for load forecasting, which is a novel load forecasting model. The ICOA algorithm effectively improves the accuracy and speed of convergence by introducing the sobol sequence and improving the pack culture trend, which is approved beneath a set of benchmark capacities from the Organized of Electrical and Hardware Engineers (IEEE) Congress on Developmental Computation (CEC) 2017. The result comparisons have moreover shown the exceptional execution of ICOA, since it could rank to begin with within the optimization of 16 benchmark. In addition, the ELM was optimized using to address the problem that the randomly formed weights and thresholds of the ELM lead to an unstable model, as well as experiment with different models using real electricity load data. The results show that the ICOA-ELM model possesses excellent accuracy and stability in short-term load forecasting. Its R2 and RMSE are 0.9970 and 0.3840 MW respectively, which are much better than ELM and LSTM. Thus, the proposed ICOA-ELM is a viable strategy for short-term load forecasting.

Keywords

• Improved Coyote Optimization Algorithm (ICOA)
• Sobol
• Extreme Learning Machine (ELM)
• Short-term Load Forecasting(STLF)