Post-processing in solar forecasting: Ten overarching thinking tools
D Yang, D van der Meer - Renewable and Sustainable Energy Reviews, 2021 - Elsevier
Forecasts are always wrong, otherwise, they are merely deterministic calculations. Besides
leveraging advanced forecasting methods, post-processing has become a standard practice …
leveraging advanced forecasting methods, post-processing has become a standard practice …
Quantile regression based probabilistic forecasting of renewable energy generation and building electrical load: A state of the art review
With the increasing penetration of renewable energy in smart grids and the increasing
building electrical load, their accurate forecasting is essential for system design, control and …
building electrical load, their accurate forecasting is essential for system design, control and …
Probabilistic day-ahead prediction of PV generation. A comparative analysis of forecasting methodologies and of the factors influencing accuracy
Photovoltaic (PV) power forecasting is essential for the integration of renewable energy
sources into the grid and for the optimisation of energy management systems. In this paper …
sources into the grid and for the optimisation of energy management systems. In this paper …
Probabilistic solar power forecasting based on weather scenario generation
Probabilistic solar power forecasting plays an important role in solar power grid integration
and power system operations. One of the most popular probabilistic solar forecasting …
and power system operations. One of the most popular probabilistic solar forecasting …
Combining quantiles of calibrated solar forecasts from ensemble numerical weather prediction
This work is concerned with optimally combining quantiles of several post-processed
versions of ensemble solar forecasts, which is new in this field. Numerical weather …
versions of ensemble solar forecasts, which is new in this field. Numerical weather …
Loss functions and metrics in deep learning. A review
J Terven, DM Cordova-Esparza… - arXiv preprint arXiv …, 2023 - arxiv.org
One of the essential components of deep learning is the choice of the loss function and
performance metrics used to train and evaluate models. This paper reviews the most …
performance metrics used to train and evaluate models. This paper reviews the most …
[HTML][HTML] Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation
The increased usage of solar energy places additional importance on forecasts of solar
radiation. Solar panel power production is primarily driven by the amount of solar radiation …
radiation. Solar panel power production is primarily driven by the amount of solar radiation …
Prediction of electrical energy consumption based on machine learning technique
R Banik, P Das, S Ray, A Biswas - Electrical engineering, 2021 - Springer
The forecast of electricity demand in recent years is becoming increasingly relevant because
of market deregulation and the introduction of renewable resources. To meet the emerging …
of market deregulation and the introduction of renewable resources. To meet the emerging …
Site-specific adjustment of a NWP-based photovoltaic production forecast
H Böök, AV Lindfors - Solar Energy, 2020 - Elsevier
Despite the increased share of photovoltaic (PV) systems, several integration challenges are
still present. For small-scale PV, the incentives for monitoring and forecasting the output are …
still present. For small-scale PV, the incentives for monitoring and forecasting the output are …
An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis
Uncertainty in predicting solar energy resources introduces major challenges in power
system management and necessitates the development of reliable probabilistic solar …
system management and necessitates the development of reliable probabilistic solar …