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Grasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. In the basic GOA, the influence of the gravity force on the updated position of every grasshopper is not considered, which possibly causes GOA to have the slower convergence speed. Based on this, the improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. One is that the gravity force is introduced into the updated position of every grasshopper in the basic GOA. And the other is that the velocity is introduced into the updated position of every grasshopper and the new position are obtained from the sum of the current position and the velocity. Then every grasshopper adopts its suitable way of the updated position on the basis of the probability. Finally, IGOA is firstly performed on the 23 classical benchmark functions and then is combined with BP neural network to establish the predicted model IGOA-BPNN by optimizing the parameters of BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. The experimental results show that IGOA is superior to the compared algorithms in term of the average values and the predicted model IGOA-BPNN has the minimal predicted errors. Therefore, the proposed IGOA is an effective and efficient algorithm for optimization. [ABSTRACT FROM AUTHOR]

The accurate and stable prediction of electricity consumption is essential for intelligent power systems in rapidly developing countries. Grey prediction model is one of choices for prediction under the condition of limited historical data. Nonetheless, it seems rather sceptical using single-variable grey prediction model to predict the dynamics of a complex system. This paper presents a novel multivariable grey prediction model based on first-order linear difference equation for long-term electricity consumption prediction. The proposed model solves the problem of parameter estimation and variable prediction deriving from different approaches through rewriting the whitenization equation of multivariable grey model (MGM(1, m)). To validate the effectiveness of the proposed hybrid model, the electricity consumption is estimated and predicted over the data from Shanxi province and Beijing city in China from 1999 to 2018. The results show that the hybrid model provides a better estimation and prediction performance compared with other prediction model for predicting electricity consumption. [ABSTRACT FROM AUTHOR]