The Experts below are selected from a list of 48513 Experts worldwide ranked by ideXlab platform

Shengzhong Feng - One of the best experts on this subject based on the ideXlab platform.

  • A novel competitive swarm optimized RBF neural network model for short-term Solar Power Generation forecasting
    Neurocomputing, 2020
    Co-Authors: Yang Zhile, Monjur Mourshed, Kailong Liu, Shengzhong Feng
    Abstract:

    Abstract Solar Power is an important renewable energy resource and acts as a major contributor to replacing fossil fuel generators and reducing carbon emissions. However, the intermittent Power output due to the uncertain Solar irradiance significantly challenges the economic integrations of Solar Generation within the existing Power system, which calls for effective forecasting methods to improve the Solar prediction accuracy. In this paper, a novel improved radial basis function neural network model is proposed and applied in forecasting the short-term Solar Power Generation. A recent proposed meta-heuristic approach named competitive swarm optimization is adopted to train the non-linear and linear parameters of the radial basis function neural network model. The proposed model has been validated in nonlinear benchmark functions and then employed in forecasting the Solar Power Generation of a real-world case study in the Netherlands. Numerical results demonstrate that the proposed competitive swarm optimized radial basis function neural network model could obtain higher accuracy compared to other counterparts and thus provides a useful tool for Solar Power forecasting.

Yang Zhile - One of the best experts on this subject based on the ideXlab platform.

  • A novel competitive swarm optimized RBF neural network model for short-term Solar Power Generation forecasting
    Neurocomputing, 2020
    Co-Authors: Yang Zhile, Monjur Mourshed, Kailong Liu, Shengzhong Feng
    Abstract:

    Abstract Solar Power is an important renewable energy resource and acts as a major contributor to replacing fossil fuel generators and reducing carbon emissions. However, the intermittent Power output due to the uncertain Solar irradiance significantly challenges the economic integrations of Solar Generation within the existing Power system, which calls for effective forecasting methods to improve the Solar prediction accuracy. In this paper, a novel improved radial basis function neural network model is proposed and applied in forecasting the short-term Solar Power Generation. A recent proposed meta-heuristic approach named competitive swarm optimization is adopted to train the non-linear and linear parameters of the radial basis function neural network model. The proposed model has been validated in nonlinear benchmark functions and then employed in forecasting the Solar Power Generation of a real-world case study in the Netherlands. Numerical results demonstrate that the proposed competitive swarm optimized radial basis function neural network model could obtain higher accuracy compared to other counterparts and thus provides a useful tool for Solar Power forecasting.

Yong Bae Kim - One of the best experts on this subject based on the ideXlab platform.

  • Daily prediction of Solar Power Generation based on weather forecast information in Korea
    IET Renewable Power Generation, 2017
    Co-Authors: Jae-gon Kim, Dong-hyuk Kim, Woo-sik Yoo, Joung-yun Lee, Yong Bae Kim
    Abstract:

    Solar panel photovoltaic (PV) systems are widely used in Korea to generate Solar energy, which is one of the most promising renewable energy sources. With regard to Solar electricity providers and a grid operator, it is critical to accurately predict Solar Power Generation for supply-demand planning in an electrical grid, which directly affects their profit. This prediction is, however, a challenging task because Solar Power Generation is weather dependent and uncontrollable. In this study, a daily prediction model based on the weather forecast information for Solar Power Generation is proposed. In the case of the proposed model, the cloud and temperature data available from the weather forecast information is used to predict the amount of Solar radiation as well as a loss adjustment factor to reflect the possible loss of Power Generation due to the degradation or failure of the PV module. Using the proposed model, Solar Power Generation for the following day can be predicted. The proposed model is embedded into a Solar PV monitoring system that is commercially used in Korea, and it is shown to perform better than the existing prediction models.

Dominik Heide - One of the best experts on this subject based on the ideXlab platform.

  • reduced storage and balancing needs in a fully renewable european Power system with excess wind and Solar Power Generation
    Renewable Energy, 2011
    Co-Authors: Dominik Heide, Marti Greine, Lude Von Breme, Clemens Hoffma
    Abstract:

    The storage and balancing needs of a simplified European Power system, which is based on wind and Solar Power Generation only, are derived from an extensive weather-driven modeling of hourly Power mismatches between Generation and load. The storage energy capacity, the annual balancing energy and the balancing Power are found to depend significantly on the mixing ratio between wind and Solar Power Generation. They decrease strongly with the overall excess Generation. At 50% excess Generation the required long-term storage energy capacity and annual balancing energy amount to 1% of the annual consumption. The required balancing Power turns out to be 25% of the average hourly load. These numbers are in agreement with current hydro storage lakes in Scandinavia and the Alps, as well as with potential hydrogen storage in mostly North-German salt caverns.

Zeyar Aung - One of the best experts on this subject based on the ideXlab platform.

  • Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation
    Energies, 2016
    Co-Authors: Azhar Ahmed Mohammed, Zeyar Aung
    Abstract:

    Probabilistic forecasting accounts for the uncertainty in prediction that arises from inaccurate input data due to measurement errors, as well as the inherent inaccuracy of a prediction model. Because of the variable nature of renewable Power Generation depending on weather conditions, probabilistic forecasting is well suited to it. For a grid-tied Solar farm, it is increasingly important to forecast the Solar Power Generation several hours ahead. In this study, we propose three different methods for ensemble probabilistic forecasting, derived from seven individual machine learning models, to generate 24-h ahead Solar Power forecasts. We have shown that while all of the individual machine learning models are more accurate than the traditional benchmark models, like autoregressive integrated moving average (ARIMA), the ensemble models offer even more accurate results than any individual machine learning model alone does. Furthermore, it is observed that running separate models on the data belonging to the same hour of the day vastly improves the accuracy of the results. Getting more accurate forecasts will help the stakeholders come up with better decisions in resource planning and control when large-scale Solar farms are integrated into the Power grid.