- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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A Simple and Reliable Photovoltaic Forecast for Reliable Power System Operation Control
摘要： Recently various forecasting methods for photovoltaic (PV) generation have been proposed in the literature. However, these standard methods cannot be successfully and widely used in general due to the fact that they require access to specialized data that are not always and everywhere readily available in practice. Furthermore, prediction accuracy of such methods tends to deteriorate specially due to data scarcity. This paper proposes a simple and reliable PV forecasting method using machine learning and neural networks. Confidence interval (CI) results are specifically provided for the local supply-demand control as well as for the robust power system security. The proposed method uses only weather forecasting data that are provided by the Japan Meteorological Agency (JMA) and which is available to the public. The proposed method maintains a high level of accuracy by using real-time correlation data between the specific target and the neighboring areas. Multiple neural networks are constructed based on a weather clustering technique. It has been confirmed through extensive simulation results that the proposed method demonstrates robustness in prediction accuracy and CI effectiveness.
关键词： Confidence intervals,Local energy management,Neural networks,Uncertainties,PV forecasting
更新于2025-09-12 10:27:22
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Confidence interval constraint based regularization framework for PET quantization
摘要： In this paper, a new generic regularized reconstruction framework based on confidence interval constraints for tomographic reconstruction is presented. As opposed to usual state-of-the-art regularization methods that try to minimize a cost function expressed as the sum of a data-fitting term and a regularization term weighted by a scalar parameter, the proposed algorithm is a two-step process. The first step concentrates on finding a set of images that relies on direct estimation of confidence intervals for each reconstructed value. Then, the second step uses confidence intervals as a constraint to choose the most appropriate candidate according to a regularization criterion. Two different constraints are proposed in this paper. The first one has the main advantage of strictly ensuring that the regularized solution will respect the interval-valued data-fitting constraint, thus preventing over-smoothing of the solution while offering interesting properties in terms of spatial and statistical bias/variance trade-off. Another regularization proposition based on the design of a smoother constraint also with appealing properties is proposed as an alternative. The competitiveness of the proposed framework is illustrated in comparison to other regularization schemes using analytical and GATE-based simulation and real PET acquisition.
关键词： confidence intervals,constrained regularization,Image reconstruction,total variation,positron emission tomography
更新于2025-09-04 15:30:14