Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting - Yin, Chen; Mao, Shuhua (Energy (Oxford), 2023)
The accurate prediction of power load is helpful to make reasonable power generation plans and scientific dispatching schemes and achieve the goal of energy saving and emission reduction. There are many factors influencing the power load, and there may be a nonlinear relationship between the power load and these influencing factors. A new fractional multivariate grey Bernoulli model, referred to as MFGBM (q, r, N), is developed in this article for the short-term prediction of power load. The fractional differential equation and fractional accumulation generation are integrated into MFGBM (q, r, N). Second, this paper improves the grey wolf algorithm to better optimize many parameters in the model. The algorithm is improved by using a chaotic Tent map to optimize the initial population composition, adding inertia weights to change the position vector of the grey wolf, and introducing a nonlinear function and Lévy flight to enhance local exploitation and global exploration ability. Finally, this paper selects the daytime and nighttime power loads in Australia and takes the electricity price, humidity, and temperature as the influencing factors to validate the prediction capability of MFGBM (q, r, N). Our findings indicate that MFGBM (q, r, N) is highly applicable to short-term power system prediction. •The multivariate grey Bernoulli model is proposed to predict short-term power load.•The improved grey wolf algorithm is proposed to avoid falling into local optimum.•3. The non-singular Caputo fractional derivative and Laplace transform are introduced.•The fractional-order operator is introduced to reduce the influence of randomness.