The White Noise Separation For Short Time Microgrid Load Forecasting

The research of microgrid short term load forecasting short-term is important for the safe and economic running of the system contains microgrid. For microgrid load, there exists an upper limit of forecast accuracy which is caused by unpredictable white noise. Generally speaking,it is an unresolved difficult problem to identify the distribution function of white noise and separate white noise from microgrid load time series .Accurate separation of white noise, can determine more optimal prediction model and more optimal prediction method, can determine the number of combination forecasting model, achieve probabilistic predictions, is of great significancefor the microgrid short term load forecastingThe main contents and results are:(1) Study the effect of wavelet denoise using four wavelet functions, hard and soft threshold function under Matlab simulation environment. Cofirm the similarities between wavelet denoising and the smoothing in time series analysis, the pros and cons of four types of wavelet denoising have something to do with the advantages and disadvantages between the moving average and exponential smoothing method which is close to normal, and infer the possibility of better smoothing coefficient in theoretically.(2) Explore the possibility of power spectrum denoising with analytical method, study the effect of power spectrum denoising using an AR(1) time series and an AR(2) time series.Wavelet denosing and differential method distinguish signal from noise by frequency, are usually used for the higher frequency noise on the lower-frequency signal sequence, while power spectrum distinguish signal from noise by amplitude, lower frequency noise on higher frequency signal sequence is still valid for power spectrum. For the case of signal and noise do not offset in frequency domain, the effect of power spectrum denoising is remarkable.(3) On the basis of empirical discovery of Smith and Wallis, give a mathematical statistics analytical explanantion for the forecast combination puzzle. Study the sample size's effect on the combined weights.(4) Study the effects of differential method and the wavelet for 48 sample load sequence from eunitel load sequence. Find differential method denoising is superior to wavelet denoising for higher frequency noise on lower frequency signal sequence. Confirm sample sequeces have larger variance.