Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision making processes in the smart power gird for better integrating renewable energies such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. Th...

This paper presents a new application of a particular machine learning technique for improving wind forecasting. The technique, known as kernel regression, is somewhat similar to fuzzy logic in that both make predictions based on the similarity of the current state to historical training states. Unlike fuzzy logic systems, kernel regression relaxes the requirement for explicit event classificat...

Short-term prediction of wind energy is by now an established field of wind power technology. For the last 15 years, our groups have worked in the field and developed short-term prediction models being used operatively at the major Danish utilities since 1996. The next step in the development of the models is to use ensemble forecasts. The ensembles are produced routinely at US NCEP (National C...

This paper examines a new time series method for very short-term wind speed forecasting. The time series forecasting model is based on Bayesian theory and structural break modeling, which could incorporate domain knowledge about wind speed as a prior. Besides this Bayesian structural break model predicts wind speed as a set of possible values, which is different from classical time series model...

As wind power is a mature and important renewable energy, wind power capacity forecasting plays an important role in renewable energy generation’s plan, investment and operation. Combined model is an effective load forecasting method; however, how to determine the weights is a hot issue. This paper proposed a combined model with differential evolution optimizing weights. The proposed model can ...

1 University of Waterloo, Ontario, Canada N2L 3G1; {a22naray, ponnu}@uwaterloo.ca 6 2 University of Massachusetts Lowell, Massachusetts, USA 01854; [email protected] 7 * Correspondence: [email protected], Tel.: +1-519-888-4567 x31450 8 Abstract: Investments in wind and solar power are driven by the aim to maximize the utilization of 9 renewable energy (RE). This results in an increas...

Accurate short-term wind power forecasting with quantification of the associated uncertainty is crucial for the management of energy systems including wind power generation. On top of the inherent uncertainty in wind speed, it is necessary to account also for the uncertainty in the relationship between wind speed and the corresponding power production, typically described by a power curve whose...

This paper investigates the performance of two simple wind power prediction models, an autoregressive one with exogenous input (ARX-model) and a neural network based one, none of which employs weather prediction data. The models are applied for predicting wind power production in three different wind parks, for which data are available. The error of the models is investigated for various foreca...

this paper presents a probabilistic optimal power flow (popf) algorithm considering different uncertainties in a smart grid. different uncertainties such as variation of nodal load, change in system configuration, measuring errors, forecasting errors, and etc. can be considered in the proposed algorithm. by increasing the penetration of the renewable energies in power systems, it is more essent...

Careful planning of the electrical power sector is of great importance since the decisions to be taken involves the commitment of large resources, with potentially serious economic risks for the electrical utility and the economy as a whole. There are different types of techniques available for analysis and prediction of randomly varying parameters. They are classified as statistical, intellige...