The ENTSO-E Transparency Platform – A review of Europe’s most ambitious electricity data platform

Hirth, Lion; Mühlenpfordt, Jonathan; Bulkeley, Marisa

doi:10.1016/j.apenergy.2018.04.048

Cited by 172 publications

(107 citation statements)

References 18 publications

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“…In terms of data quality, there is confidence in the model's input data: [21] shows that precipitation and temperature variables from the ERA5 dataset can produce results equivalent to observational data in hydrological modelling from North-American catchments; [6] on the ENTSO-E dataset also highlights that it is the most ambitious global open source dataset for energy data.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…This inconsistency, for example, may lead to capacity factors (actual generation divided by installed capacity) larger than 1 (this can be the case for German reservoir-based and Norwegian run-of-river generation for instance). A description of the ENTSO-E Transparency Platform and its limitations can be found in [6]. Since this inconsistency derives from the process of reporting data which we cannot intervene, we decided to use the data as provided by ENTSO-E without any pre-processing.…”

Section: Introductionmentioning

confidence: 99%

“…Hydro pump storage was not considered as it contributes only a very small proportion and heavily depends on human management rather than climate conditions. Since energy generation data for specific hydro power plants in Europe are not publicly available, we used the aggregated publicly available data obtained from the European Network of Transmission System Operators for Electricity (ENTSO-E) Transparency Platform [6]. The focus here is mainly at the country level, a large enough spatial scale to cover relevant climate characteristics such as precipitation or snow contributing to hydro power plants.…”

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confidence: 99%

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Reconstruction of Multidecadal Country-Aggregated Hydro Power Generation in Europe Based on a Random Forest Model

Dubus

Felice

et al. 2020

Energies

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Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments.Energies 2020, 13, 1786 2 of 17 for individual power plants. There are several explanations for this approach: (i) modelling tends to be thought of as a bottom-up approach, therefore starting from individual power plants (to e.g., assist with their operations) rather than at the entire country or pan-European level; (ii) the impacts of climate variables on hydro power are varied and complex, and many factors contribute to these impacts such as watershed characteristics, the river flow rate, the evaporation process, and human factors, and hence, using only climate data is not sufficient to predict hydro power generation at the desired accuracy for plant-level operational purposes; (iii) hydro power generation at the European level has only recently started to become publicly available (as further discussed below); (iv) the quality of climate data as provided by reanalysis (i.e., climate reconstructions), and particularly precipitation, has been improving significantly only in recent years.Wind and solar power play an increasingly important role in a low-carbon energy plan. However, their weather-dependent nature requires a more stable energy source, together with storage solutions, to cope with climate variability. Hydro power is a good candidate with its ability to dispatch or store electricity according to demand. For example, [4] sho...

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Section: Summary and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Reconstruction of Multidecadal Country-Aggregated Hydro Power Generation in Europe Based on a Random Forest Model

Dubus

Felice

et al. 2020

Energies

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“…On a high hierarchy level, usually, the transmission system operator (TSO) and sometimes the distribution system operator (DSO) are responsible for the metering and publishing of the load in the corresponding electricity system. When it comes to the details, there exists a wide range of definitions for electrical load; see, e.g., [2,3]. In many countries, there exist accounting rules for the system operator, which define the metering process for billing and management purposes.…”

Section: Introductionmentioning

confidence: 99%

Load Nowcasting: Predicting Actuals with Limited Data

Ziel

2020

Energies

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We introduce the problem of load nowcasting to the energy forecasting literature. The recent load of the objective area is predicted based on limited available metering data within this area. Thus, slightly different from load forecasting, we are predicting the recent past using limited available metering data from the supply side of the system. Next, to an industry benchmark model, we introduce multiple high-dimensional models for providing more accurate predictions. They evaluate metered interconnector and generation unit data of different types like wind and solar power, storages, and nuclear and fossil power plants. Additionally, we augment the model by seasonal and autoregressive components to improve the nowcasting performance. We consider multiple estimation techniques based on the lassoand ridge and study the impact of the choice of the training/calibration period. The methodology is applied to a European TSO dataset from 2014 to 2019. The overall results show that in comparison to the industry benchmark, an accuracy improvement in terms of MAE and RMSE of about 60% is achieved. The best model is based on the ridge estimator and uses a specific non-standard shrinkage target. Due to the linear model structure, we can easily interpret the model output.

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“…In general, the public availability of European electricity system data has increased significantly during the last years due to rising transparency regulation. An example of this has been the introduction of the ENTSO-E Transparency Platform [26] (see [27] for a review). Yet, data is provided by many different institutions and thus often dispersed over various repositories and formatted in inconsistent ways.…”

Section: Introductionmentioning

confidence: 99%

Open Power System Data – Frictionless data for electricity system modelling

et al. 2019

Self Cite

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The quality of electricity system modelling heavily depends on the input data used. Although a lot of data is publicly available, it is often dispersed, tedious to process and partly contains errors. We argue that a central provision of input data for modelling has the character of a public good: it reduces overall societal costs for quantitative energy research as redundant work is avoided, and it improves transparency and reproducibility in electricity system modelling. This paper describes the Open Power System Data platform that aims at realising the efficiency and quality gains of centralised data provision by collecting, checking, processing, aggregating, documenting and publishing data required by most modellers. We conclude that the platform can provide substantial benefits to energy system analysis by raising efficiency of data pre-processing, providing a method for making data pre-processing for energy system modelling traceable, flexible and reproducible and improving the quality of original data published by data providers.

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The ENTSO-E Transparency Platform – A review of Europe’s most ambitious electricity data platform

Cited by 172 publications

References 18 publications

Reconstruction of Multidecadal Country-Aggregated Hydro Power Generation in Europe Based on a Random Forest Model

Reconstruction of Multidecadal Country-Aggregated Hydro Power Generation in Europe Based on a Random Forest Model

Load Nowcasting: Predicting Actuals with Limited Data

Open Power System Data – Frictionless data for electricity system modelling

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