A Fast and Stable Forecasting Model to Forecast Power Load

Abstract

As the traditional gray forecasting model GM(1, 1) has poor performance in forecasting the fastgrowing power load, we present a chaotic co-evolutionary particle swarm optimization (CCPSO) algorithm, one with better e±ciency than the PSO algorithm. Based on the GM(1, 1) model, the CCPSO algorithm is adopted to solve the values of parameters a and b in GM(1, 1) model. This is how the way we come up with the CCPSO algorithm-based GM. As can be seen ¶ Corresponding author.from experimental results of case simulation on the power consumption in three regions, the CCPSO-GM model is better than the other four forecasting models, which attests its wide applicability and high forecasting accuracy.

Application of single hidden layer fuzzy recurrent wavelet neural network to multi-physical parameter monitoring and on-line analysis of high voltage power equipment

“…The system adopts the single light source and the dual wavelength optical structure, and further develops the SF6 gas decomposition product optical sensor detection system. The system adopts single light source and dual wavelength structure, eliminates the unstable factors of the measurement system, and has high measurement accuracy [12]. On the basis of effectively obtaining the time series of SF6 decomposition product volume fraction, the SLFRWNN(1, N) model is applied to fit and predict it, and the high-precision fitting of gas volume fraction and the prediction and analysis of gas concentration in wide area time domain are realized.…”

Gri Sistem Teorisi ve Enerji Tüketim Modellemesinde Bir Uygulama

“…Bu modeller topluluğu içerisinde GM (1,1) modeli ön plana çıkmaktadır. GM (1,1) modeline ilişkin olarak model parametrelerinin optimize edilmesi ( [8], [9]), hibrit GM (1,1) model önerileri ( [10], [11], [12]), GM(1,1) model açılımları ( [13], [14], [15]) ve farklı başlangıç koşullarının önerilmesi ( [16], [17]) gibi farklı bakış açılarından birçok çalışma yapılmıştır.…”

Maximum Power Demand Prediction Using Fbprophet With Adaptive Kalman Filtering

“…MPD prediction methods mainly consist of the grayscale prediction method, neural network method and time series method. Among them, the grayscale prediction method is suitable for considering a variety of uncertain factors and analyzing the correlation between system factors [9], [10]. In [11], [12], the Elman and Feed Forward Neural Network (FFNN) were used respectively to predict the MPD to realize energy saving.…”