| 1. |
- van der Meer, Dennis, et al.
(författare)
-
Clear-sky index space-time trajectories from probabilistic solar forecasts : Comparing promising copulas
- 2020
-
Ingår i: Journal of Renewable and Sustainable Energy. - : AMER INST PHYSICS. - 1941-7012. ; 12:2
-
Tidskriftsartikel (refereegranskat)abstract
- Short-term probabilistic solar forecasts are an important tool in decision-making processes in which uncertainty plays a non-negligible role. Purely statistical models that produce temporal or spatiotemporal probabilistic solar forecasts are generally trained individually, and the combined forecasts therefore lack the temporal or spatiotemporal correlation present in the data. To recover the spatiotemporal dependence structure, a copula can be employed, which constructs a multivariate distribution from which spatially and temporally correlated uniform random numbers can be sampled, which in turn can be used to generate the so-called space-time trajectories via the inverse probability integral transform. In this study, we employ the recently introduced ultra-fast preselection algorithm to leverage the spatiotemporal information present in a pyranometer network and compare its accuracy to that of quantile regression forecasts that only consider temporal information. We show that the preselection algorithm improves both the calibration and sharpness of the predictive distributions. Furthermore, we employ four copulas, i.e., (1) Gaussian, (2) Student-t, (3) Clayton, and (4) empirical, to generate space-time trajectories. The results highlight the necessity to rigorously assess the calibration of the space-time trajectories and the correct modeling of the spatiotemporal dependence structure, which we show through techniques introduced in atmospheric sciences. The code used to generate the results in this study can be found at https://github.com/DWvanderMeer/SpaceTimeTrajectories.
|
|
| 2. |
- Yang, Dazhi, et al.
(författare)
-
Post-processing in solar forecasting : Ten overarching thinking tools
- 2021
-
Ingår i: Renewable & sustainable energy reviews. - : Elsevier. - 1364-0321 .- 1879-0690. ; 140
-
Tidskriftsartikel (refereegranskat)abstract
- Forecasts are always wrong, otherwise, they are merely deterministic calculations. Besides leveraging advanced forecasting methods, post-processing has become a standard practice for solar forecasters to improve the initial forecasts. In this review, the post-processing task is divided into four categories: (1) deterministic-todeterministic (D2D) post-processing, (2) probabilistic-to-deterministic (P2D) post-processing, (3) deterministic-to-probabilistic (D2P) post-processing, and (4) probabilistic-to-probabilistic (P2P) post-processing. Additionally, a total of ten overarching thinking tools, namely, (1) regression (D2D), (2) filtering (D2D), (3) resolution change (D2D), (4) summarizing predictive distribution (P2D), (5) combining deterministic forecasts (P2D), (6) analog ensemble (D2P), (7) method of dressing (D2P), (8) probabilistic regression (D2P), (9) calibrating ensemble forecasts (P2P), and (10) combining probabilistic forecasts (P2P), are proposed. These thinking tools can be thought of as the "style" or "mechanism" of post-processing. In that, the utilization of thinking tools circumvents the common pitfalls of classifying the literature by methods (e.g., statistics, machine-learning, or numerical weather prediction), which often leads to a "who used what method" type of roster review that is clearly ineffective, non-exhaustive, and dull. When myriads of post-processing methods are mapped to countable few thinking tools, it allows solar forecasters to enumerate the styles of adjustment that could be performed on a set of initial forecasts, which makes a post-processing task clearly goal-driven. Besides the thinking tools, this paper also emphasizes on the value of post-processing, and provides an outlook for future research. Although this paper is revolved around solar, the materials herein discussed can also be applied to wind and other forecasting areas.
|
|
| 3. |
- Yang, Dazhi, et al.
(författare)
-
Probabilistic solar forecasting benchmarks on a standardized dataset at Folsom, California
- 2020
-
Ingår i: Solar Energy. - : Elsevier BV. - 0038-092X .- 1471-1257. ; 206, s. 628-639
-
Tidskriftsartikel (refereegranskat)abstract
- The present paper echos a recent data article, "A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods" [J. Renewable Sustainable Energy 11, 036102 (2019)]. The carefully composed dataset by Pedro, Larson, and Coimbra (PLC) presents a rare opportunity for solar forecasters to develop transparent and reproducible algorithms that can bring incremental contributions to the field. In their original paper, data from four different sources, namely, ground-based measurements, sky-camera images, satellite-imagery features, and numerical weather prediction outputs, were arranged in a machine-learning-ready setup. Subsequently, several benchmarks for deterministic forecasting were set forth, for intra-hour, intra-day, and day-ahead scenarios. Nonetheless, a weather forecast is intrinsically five-dimensional, spanning space, time, and probability. In this regard, five reference methods for probabilistic forecasting: (1) complete-history persistence ensemble, (2) Markov-chain mixture model, (3) ordinary least squares, (4) analog ensemble, and (5) quantile regression, are applied to the PLC dataset. The R code provided in this paper follows the structure of the original Python code precisely, facilitating those solar forecasters who are not familiar with Python but have a statistics background.
|
|
| 4. |
- Yang, Dazhi, et al.
(författare)
-
Verification of deterministic solar forecasts
- 2020
-
Ingår i: Solar Energy. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0038-092X .- 1471-1257. ; 210, s. 20-37
-
Tidskriftsartikel (refereegranskat)abstract
- The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing sub-domain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy-Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows-with appropriate caveats-comparison of forecasts made using different models, across different locations and time periods.
|
|
| 5. |
- Zainali, Sebastian, 1995-, et al.
(författare)
-
Site adaptation with machine learning for a Northern Europe gridded global solar irradiance product
- 2023
-
Ingår i: Energy and AI. - 2666-5468. ; 15
-
Tidskriftsartikel (refereegranskat)abstract
- Gridded global horizontal irradiance (GHI) databases are fundamental for analysing solar energy applications' technical and economic aspects, particularly photovoltaic applications. Today, there exist numerous gridded GHI databases whose quality has been thoroughly validated against ground-based irradiance measurements. Nonetheless, databases that generate data at latitudes above 65˚ are few, and those available gridded irradiance products, which are either reanalysis or based on polar orbiters, such as ERA5, COSMO-REA6, or CM SAF CLARA-A2, generally have lower quality or a coarser time resolution than those gridded irradiance products based on geostationary satellites. Amongst the high-latitude gridded GHI databases, the STRÅNG model developed by the Swedish Meteorological and Hydrological Institute (SMHI) is likely the most accurate one, providing data across Sweden. To further enhance the product quality, the calibration technique called "site adaptation" is herein used to improve the STRÅNG dataset, which seeks to adjust a long period of low-quality gridded irradiance estimates based on a short period of high-quality irradiance measurements. This study introduces a novel approach for site adaptation of solar irradiance based on machine learning techniques, which differs from the conventional statistical methods used in previous studies. Seven machine-learning algorithms have been analysed and compared with conventional statistical approaches to identify Sweden's most accurate algorithms for site adaptation. Solar irradiance data gathered from three weather stations of SMHI is used for training and validation. The results show that machine learning can substantially improve the STRÅNG model's accuracy. However, due to the spatiotemporal heterogeneity in model performance, no universal machine learning model can be identified, which suggests that site adaptation is a location-dependant procedure.
|
|
