The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decom...

A campus based smart micro grid model that combined power and heat is presented. In this study, optimal scheduling of distributed generations inside these smart micro grids is determined based on demand forecasting, solar power forecasting and wind power forecasting by artificial neural network (ANN). The proposed model is then evaluated through economical efficiency, environment efficiency and...

Forecasting wind power is an important part of a successful integration of wind power into the power grid. Forecasts with lead times longer than 6 hours are generally made by using statistical methods to postprocess forecasts from numerical weather prediction systems. Two major problems that complicate this approach are the nonlinear relationship between wind speed and power production and the ...

To support large-scale integration of wind power into electric energy systems, state-of-the-art wind speed forecasting methods should be able to provide accurate and adequate information to enable efficient, reliable, and cost-effective scheduling of wind power. Here, we incorporate space-time wind forecasts into electric power system scheduling. First, we propose a modified regime-switching, s...

The optimal design of offering strategies for wind power producers is commonly based on unconditional (and, hence, constant) expectation values for prices in real-time markets, directly defining their loss function in a stochastic optimization framework. This is why it may certainly be advantageous to account for the seasonal and dynamic behavior of such prices, hence translating to time-varyin...

A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP). A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain...

A novel short-term wind energy forecasting method, which is being developed under the UBDIBM renewable energy modeling initiative, is described in this paper. The paper starts with a brief review on the existing forecasting methods. Prediction models based on the physical (derived from Numerical Whether Prediction models) and Time Series approaches are discussed. The prediction errors under the...

As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational ...

Ireland and other countries in the EU have binding targets for production of energy from renewable sources by 2020. Ireland’s Renewable Energy Action Plan aims to meet this target by producing 40% of electrical energy from renewable sources and most of this will come from wind power. In order to forecast the amount of wind power capacity required, it is necessary to forecast the amount of wind ...

Wind speed forecasting can accurately improve prediction efficiency of wind power in wind farm, decrease failure probability of wind turbine, and extend life cycle. An innovative algorithm is proposed to optimize both the parameters of least squares support vector machine (LSSVM) and the procedure of finding sparse support vector. Firstly, the defects of support vector are analyzed. Then inequa...