Wind Power Forecasting Using Wavelet Transforms and Neural Networks with Tapped Delay

With an objective to improve wind power estimation accuracy and reliability,this paper presents Linear Neural Networks with Tapped Delay(LNNTD)in combination with wavelet transform(WT)for probabilistic wind power forecasting in a time series framework.For comparison purposes,results of the proposed model are compared with the benchmark model,different neural networks and WT based models considering performance indices such as accuracy,execution time and R^(2) statistic.For the reliability and proper validation of the proposed model,this paper highlights the probabilistic forecast attributes at different skill tests.The historical data of the Ontario Electricity Market(OEM)for the period 2011–2014 were used and tested for two years from November 2012 to October 2014 with one month moving window considering all seasonal aspects.The experimental results clearly show that the results of the proposed model have been found to be better than others.

A comprehensive review and analysis of solar forecasting techniques

In the last two decades,renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic,industrial,and agriculture sectors.Solar forecasting plays a vital role in smooth operation,scheduling,and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants.Numerous models and techniques have been developed in short,mid and long-term solar forecasting.This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature,by mainly focusing on investigating the influence of meteorological variables,time horizon,climatic zone,pre-processing techniques,air pollution,and sample size on the complexity and accuracy of the model.To make the paper reader-friendly,it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication,time resolution,input parameters,forecasted parameters,error metrics,and performance.The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities.Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data.Besides,it also discusses the diverse key constituents that affect the accuracy of a model.It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.