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The EU Copernicus Climate Change Service (C3S) has produced an operational climate service, called C3S Energy, designed to enable the energy industry and policy makers to assess the impacts of climate variability and climate change on the energy sector in Europe. The C3S Energy service covers different time horizons, for the past forty years and the future. It provides time series of electricity demand and supply from wind, solar photovoltaic and hydro power, and can be used for recent trends analysis, seasonal outlooks or the assessment of climate change impacts on energy mixes in the long-term.This paper introduces this dataset, with a focus on the design and validation of the energy conversion models, based on ENTSO-E energy data and the ERA5 climate reanalysis. Flexibility and coherence across all countries have been privileged upon models’ accuracy. However, the comparison with ENTSO-E data shows that the models provide plausible energy indicators and, in particular, allow to compare climate variability effects on power demand and generation in an homogenous approach all over Europe.

A key objective of the Added Value of Seasonal Climate Forecasts for Integrated Risk Management Decisions (SECLI-FIRM, www.secli-firm.eu) project is the optimisation of the performance of seasonal climate forecasts provided by many producing centers, in a Grand Multi-Model approach, for predictands relevant for the specific case studies considered in SECLI-FIRM.The Grand Multi-Model Ensemble (MME) consists of the five Seasonal Prediction Systems (SPSs) provided by the European Copernicus C3S and a selection of other five SPSs independently developed by centres outside Europe, four by the North American (NMME) plus the SPS by the Japan Meteorological Agency (JMA).All the possible multi-model combinations have been evaluated showing that, in general, only a limited number of SPSs is required to obtain the maximum attainable performance. Although the selection of models that perform better is usually different depending on the region/phenomenon under consideration, it is shown that the performance of the Grand-MME seasonal predictions is enhanced with the increase of the independence of the contributing SPSs, i.e. by mixing European SPSs with those from NMME-JMA.Starting from the definition of the Brier score a novel metric has been developed, named the Brier score covariance (BScov), which estimates the relative independence of the prediction systems. BScov is used to quantify independence among the SPSs and, together with probabilistic skill metrics, used to develop a strategy for the identification of the combinations that optimize the probabilistic performance of seasonal predictions for the study cases.

Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single model ensembles. The potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. To this aim, a novel methodology has been developed to assess the relative independence of the prediction systems in the probabilistic information they provide.We considered the Copernicus C3S seasonal forecasts product considering the one-month lead retrospective seasonal predictions for boreal summer and boreal winter seasons (1st May and 1st November start dates, i.e. June-July-August, JJA and December-January-February, DJF). We analysed the seasonal hindcasts in terms of deterministic and probabilistic scores with a particular focus on continental areas, since little evaluation has been performed so far over land domains that is where most of the applications of seasonal forecasts are based. The most relevant target variables of interest for the energy users have been considered and skill differences between the prediction systems have been analysed together with related possible sources of predictability. The analysis evidenced the importance of snow-albedo processes for temperature predictions in DJF and the effect of the atmospheric dynamics through moisture convergence for the prediction of surface solar radiation in JJA. A new metric, the Brier Score Covariance, designed to quantify the probabilistic independence among the models, has been developed and applied to optimize model selection and combination strategies with a particular focus on the most relevant variables for energy applications.

The operation and economic profitability of modern energy systems is constrained by the availability of renewable energy and water resources. Lower water availability due to climate change, higher demand and increased water consumption for non-energy and energy needs may cause problems in Africa. In most African power systems, hydropower is a dominant renewable energy resource, and interconnection capacities are usually limited or unreliable. This paper describes a new modelling framework for analysing the water-energy nexus in the African Power Pools. This framework includes soft linking between two models: the LISFLOOD model is used to generate hydrological inputs and the Dispa-SET model is used for mid-term hydrothermal coordination and optimal unit commitment and power dispatch over the whole African continent. The results show a good agreement between the model outputs and the historical values, despite data-related limitations. Furthermore, the simulations provide hourly time series of electricity generation at the plant level in a robust way. It appears that some African power pools heavily rely on the availability of freshwater resources, while others are less dependent. In the long term, the dependence of the power system on water resources is likely to increase to meet the increasing electricity demand in Africa.

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (twentieth century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2 m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration. This work was supported by the European Union Seventh Framework Programme (FP7/2007-13) under Grant 308378 (SPECS Project; http://specs-fp7.eu/). The ECMWF experiments were supported by the EU-FP7 ImagineS project (http://fp7-imagines. eu/) in support to the Copernicus Global land. Further support was provided to this work by the European Union’s Horizon 2020 research and innovation programme under grant agreement N. 641816 (CRESCENDO project; http://crescendoproject.eu/) and under grant agreement N. 704585 (PROCEED project). Acknowledgement is made for the use of ECMWF’s computing and archive facilities in this research (special project SPITALES).

Distributed generation from wind and solar acts on regional electric demand as a reduced consumption, giving rise to a “load shadowing effect”. The net load becomes much more difficult to predict due to its dependence on the meteorological conditions. As a consequence, the growing penetration of variable generation increases the imbalance between demand and scheduled supply (net load forecast) and the reserve margins (net load uncertainty). The aim of this work is to quantify the benefit of the use of advanced probabilistic approaches rather than a traditional time-series method to assess the day-ahead reserves. For this purpose, several methods for load and net load uncertainty assessment have been developed and applied to a real case study considering also future solar penetration scenarios. The results show that, when forecasting only the load both traditional and probabilistic methods exhibit similar accuracy. Instead, in the case of net load prediction, i.e. when solar power is present, the probabilistic forecast can effectively limit the reserve margin needed to arrange the imbalance between residual demand and supply. The developed probabilistic approach provides a notable reduction of the Following Reserve which increases with the solar penetration: from 32.5% to 68.3% at 7% and 45% of penetration.

Solar photovoltaic energy is widespread worldwide and particularly in Europe, which became in 2016 the first region in the world to pass the 100 GW of installed capacity. As with all the renewable energy sources, for an effective management of solar power, it is essential to have reliable and accurate information about weather/climate conditions that affect the production of electricity. Operations in the solar energy industry are normally based on daily (or intra-daily) forecasts. Nevertheless, information about the incoming months can be relevant to support and inform operational and maintenance activities. This paper discusses a methodology to assess whether a seasonal climate forecast can provide a useful prediction for a specific sector, in this paper the European solar power industry. After evaluating the quality of the forecasts in providing probabilistic information for solar radiation, we describe how to assess their potential usefulness for a generic user by proposing an approach that takes into account not only their accuracy but also other potentially relevant factors. This approach is called index of opportunity and is then illustrated by presenting an example for the European solar power sector. The index of opportunity provides indications about where and when seasonal climate forecasts can benefit the decision-making in the photovoltaic sector. Even more importantly, it suggests an approach on how to evaluate their usefulness for the user's decision-making. This approach has the advantage of not limiting the definition of the usefulness only to the quality of the forecasts but rather considering, in an explicit way, all the factors that must be combined with the forecast's quality to define what is useful or not for the user.

In this paper we analyse and quantify the impact of North Atlantic Oscillation (NAO) on the daily hydropower generation in the European countries. We create a consistent dataset of hydropower generation using a machine- learning methodology, this activity is an output of the Copernicus Climate Change Service ECEM contract. The model is set up using generation data on 2015-2016, and then an artificial dataset was built over the period 1979- 2017, as if the installed capacity, that of 2017, had been the same all along the period, using reanalysis data for the meteorological variables. This synthetic dataset thus generated has been used to analyse the changes in national hydro-power generation (both for run-of-river and reservoir-based plants) be- tween years when NAO is positive and when it is negative, over Europe. The results show how the majority of countries in southern Europe experience a reduction in generation during NAO positive phases. In particular the Iberian peninsula shows a clear sensitivity with change of both the hydro-power generation typologies in the range -8% – -4% in terms of capacity factor. This work also shows the benefit of having a pan-European dataset of energy vari- ables spanning multiple decades to analyse the impact of climate phenomena on the European energy sector.

The increased penetration of photovoltaic power introduces new challenges for the stability of the electrical grid, both at the local and national level. Many different effects are caused by high solar power injection into the electric grid. Among them, the increased risk of imbalance between the actual and scheduled power transmission is of particular relevance. The consequence is the need to exchange larger amounts of dispatchable power on the balancing energy market. The aim of this work is to analyze and quantify the effects of PV penetration in a target region and to evaluate the energy and economic benefits of using day-ahead PV forecast for power transmission scheduling. For this purpose, we developed several data-driven methods for transmission scheduling that include day-ahead PV power forecasts. We compared the resulting operational imbalances from these new models against two reference models currently used by the local grid operators. In the case of no PV generation in the target area, the more accurate reference model leads to an imbalance of 3.6% of the peak power transmission while more accurate data-driven method reduces the imbalance to 3.2%. When the distributed PV capacity is not zero, the imbalance of the reference model grows from 5.15% (at the current penetration of 7%) to 9.8% (at the maximum planned regional penetration of 45%). When we apply the new scheduling model, imbalances are reduced to respectively 3.5% and 5.8% at 7% and 45% of penetration. Since in Italy the costs of imbalances resulting from distributed PV are borne by ratepayers, these costs are estimated to be respectively 2.3% and 15% of the average electricity bill at 7% and 45% penetration if the reference scheduling is used. When applying the new model these costs are respectively reduced to 1.2% and 8.5%. © 2018 Elsevier Ltd

Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model ensembles. Previous works suggested that the potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. In this work we combine the two MME Seasonal Prediction Systems (SPSs) independently developed by the European (ENSEMBLES) and by the Asian-Pacific (APCC/CliPAS) communities. To this aim, all the possible multi-model combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from APCC/CliPAS have been evaluated. The grand ENSEMBLES-APCC/CliPAS MME enhances significantly the skill in predicting 2m temperature and precipitation compared to previous estimates from the contributing MMEs. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and APCC/CliPAS models and that only a limited number of SPSs is required to obtain the maximum performance. The number and selection of models that perform better is usually different depending on the region/phenomenon under consideration so that all models are useful in some cases. It is shown that the incremental performance contribution tends to be higher when adding one model from ENSEMBLES to APCC/CliPAS MMEs and vice versa, confirming that the benefit of using MMEs amplifies with the increase of the independence the contributing models. To verify the above results for a real world application, the Grand ENSEMBLES-APCC/CliPAS MME is used to predict retrospective energy demand over Italy as provided by TERNA (Italian Transmission System Operator) for the period 1990–2007. The results demonstrate the useful application of MME seasonal predictions for energy demand forecasting over Italy. It is shown a significant enhancement of the potential economic value of forecasting energy demand when using the better combinations from the Grand MME by comparison to the maximum value obtained from the better combinations of each of the two contributing MMEs. The above results demonstrate for the first time the potential of the Grand MME to significantly contribute in obtaining useful predictions at the seasonal time-scale.

Accurate and robust short-term load forecasting plays a significant role in electric power operations. This paper proposes a variant of genetic programming, improved by incorporating semantic awareness in algorithm, to address a short term load forecasting problem. The objective is to automatically generate models that could effectively and reliably predict energy consumption. The presented results, obtained considering a particularly interesting case of the South Italy area, show that the proposed approach outperforms state of the art methods. Hence, the proposed approach reveals appropriate for the problem of forecasting electricity consumption. This study, besides providing an important contribution to the energy load forecasting, confirms the suitability of genetic programming improved with semantic methods in addressing complex real-life applications. © 2014 Elsevier B.V.

Air temperature is an effective predictor for electricity demand, especially during hot periods where the need of electric air conditioning can be high. This paper presents for the first time an assessment of the use of seasonal climate forecasts of temperature for medium-term electricity demand prediction. The retrospective seasonal climate forecasts provided by ECWMF (European Centre for Medium-Range Weather Forecasts) are used to forecast the June-July Italian electricity demand for the period 1990-2007.We find a relationship between summer (June-July) average temperature patterns over Europe and Italian electricity demand using both a linear and non-linear regression approach. With the aim to evaluate the potential usefulness of the information contained into the climate ensemble forecast, the analysis is extended considering a probabilistic approach.Results show that, especially in the Center-South of Italy, seasonal forecasts of temperature issued in May lead to a significant correlation coefficient of electricity demand greater than 0.6 for the summer period. The average correlation obtained from seasonal forecasts is 0.53 for the temperature predicted in May and 0.19 for the predictions issued in April for the linear model, while the non-linear approach leads to the coefficients of 0.62 and 0.36 respectively. For the probabilistic approach, seasonal forecasts exhibit a positive and significant skill-score in predicting the demand above/below the upper/lower tercile in many regions.This work is a significant progress in understanding the relationship between temperature and electricity demand. It is shown that much of the predictable electricity demand anomaly over Italy is connected with so-called heat-waves (i.e. long lasting positive temperature anomalies) over Europe. © 2014 Elsevier Ltd.

International audience; European Climatic Energy Mixes (ECEM) is a Copernicus Climate Change Service (C3S) activity which isdeveloping, in close collaboration with the energy sector, a proof-of-concept model – or demonstrator. Its purposeis to enable the energy industry and policy makers to assess how well energy supply will meet demand in Europeover different time horizons, focusing on the role climate has on energy supply and demand.This presentation sums up how energy time series were modelled and computed at country level and dailytime step for the whole Europe.Bias-adjusted Essential Climate Variables from ERA-Interim over a European domain for 1979-2016 were used tocalculate electricity demand and generation from wind, solar and hydro power. Different strategies were adopteddepending on the target variable, and the availability of measured data to calibrate and validate the energy models.For demand, individual models were set up for each country, using a general approach based on GeneralizedAdditive Models. Hydro power generation could not be modelled precisely using a physical model at plant level,due to the lack of a plants database; hence a simplified statistical model approach was chosen, and gives goodresults. Wind and solar power generation were modelled with both statistical and physical models.Results of these different models and approaches will be presnted, and compared to actual measurements orestimations of electricity demand and supply. Comparison will also be made with other projects.We will then present how the produced time series are integrated into the ECEM demonstrator, and howsuch information can be exploited to analyze and understand the supply/demand balance problem at the Europeanscale. We will also discuss some critical needs for the further development of such services, including theavailability of energy data.

This study fills in knowledge gaps for the European air-conditioning (AC) market, which is fundamentally important to raising awareness about primary energy utilization. In contrast to space heating (SH) and domestic hot water (DHW) preparation, the European Union (EU) AC market is barely explored in scientific literature. While the focus of previous research has been on the residential sector, a shortfall of data for the services (wholesale and retail, offices, education, health, hotels and bars) exists. In this paper, data describing the actual space cooling (SC) market in Europe (quantity of SC units, equivalent full-load hours, installed capacities, seasonal energy efficiency values as well as cooled floor area per AC type and/or sector) is collected and explored using a bottom-up approach. Results indicate that SC is responsible for a significant portion of EU electricity consumption in households (nearly 5%) and even more in the service sector (~13%). Energy consumption for SC in the EU28 appears to be more than 140 TWh/y. The quantification of the European AC consumption shows a significant difference between the service and residential sectors: about 115 versus 25 TWh/y respectively. The SC market in Europe is characterized by a high potential for growth, especially in households., We would like to convey our deepest appreciation to the Horizon 2020 Hotmaps Project (Grant Agreement No. 723677), which provided the funding to carry out the present investigation. We are thankful to Pierre Iachetti (Eurac Research), who edited the entire text of the present investigation. Finally, our gratitude goes to Amy Segata (Eurac Research) for designing the graphs of this study.

International audience; ECEM is a Copernicus Climate Change Services project focused on the role climate has on the European energymix (supply/demand).Climate data is converted into energy variables (energy demand and production for renewable energy sources) at country-level using statistical and physical models. Energy dataset have been gathered from a wide range of sources (e.g. ENTSO-E data and individual countries TSOs’ data) to build a common database to be used to analyse the relationship between climate information and the energy sector. For the climate side, ERA-INTERIM reanalysis has been used for the essential climate variables after a bias-correction procedure. Modelling results reveal the uniqueness of each national power system, underlining the need of a diverse set of modelling methodologies. In fact, both physical and statistical models have been used, calibrated with all the available observed climate and energy data. Results demonstrate the good performance of the climate data as predictors for the target energy variables: cross-validation error for daily national electricity demand is below 2.5% while for RES generation it is commonly below 10%. Furthermore, the work has highlighted the need of high-quality and complete metadata to maximise the impact of the produced data on the targeted user community.

Electricity demand forecasting is a critical task for energy management of power grids. Due to the wide use of refrigeration and residential air-conditioning devices, electricity demand in Italy is influenced by weather conditions, especially during summer. This paper performs daily load forecasting for Italy through statistical modeling with the aim of studying the influence of temperature. The actual capability of available weather forecasts to contribute in predicting electricity loads is evaluated by using weather data from numerical weather prediction (NWP) models. Time-series models have been used and compared with a naive predictor on working-days daily load during June and July in years 2003-2009 considering lead-times between one and five days. Results are analyzed both at the national level and at regional scale, using unprecedented historical load data provided by the Italian transmission grid manager. It is shown that the use of weather data provided by NWP models leads to performance improvements, especially for the hottest areas where the use of electricity is more heavily influenced by temperature. Furthermore, by observing the gap between load forecast models using reanalysis and operational forecast weather data we can obtain some clues about the limitations of the weather forecast models we used on specific geographic areas in Italy. © 2013 Elsevier B.V.

Photovoltaic (PV) power forecasting has the potential to mitigate some of effects of resource variability caused by high solar power penetration into the electricity grid. Two main methods are currently used for PV power generation forecast: (i) a deterministic approach that uses physics-based models requiring detailed PV plant information and (ii) a data-driven approach based on statistical or stochastic machine learning techniques needing historical power measurements. The main goal of this work is to analyze the accuracy of these different approaches. Deterministic and stochastic models for dayahead PV generation forecast were developed, and a detailed error analysis was performed. Four years of site measurements were used to train and test the models. Numerical weather prediction (NWP) data generated by the weather research and forecasting (WRF) model were used as input. Additionally, a new parameter, the clear sky performance index, is defined. This index is equivalent to the clear sky index for PV power generation forecast, and it is here used in conjunction to the stochastic and persistence models. The stochastic model not only was able to correct NWP bias errors but it also provided a better irradiance transposition on the PV plane. The deterministic and stochastic models yield day-ahead forecast skills with respect to persistence of 35% and 39%, respectively. Copyright © 2017 by ASME.

The growing photovoltaic generation results in a stochastic variability of the electric demand that could compromise the stability of the grid, increase the amount of energy reserve and the energy imbalance cost. On regional scale, the estimation of the solar power generation from the real time environmental conditions and the solar power forecast is essential for Distribution System Operators, Transmission System Operator, energy traders, and Aggregators. In this context, a new upscaling method was developed and used for estimation and forecast of the photovoltaic distributed generation in a small area of Italy with high photovoltaic penetration. It was based on spatial clustering of the PV fleet and neural networks models that input satellite or numerical weather prediction data (centered on cluster centroids) to estimate or predict the regional solar generation. Two different approaches were investigated. The simplest and more accurate approach requires a low computational effort and very few input information should be provided by users. The power estimation model provided a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 h) obtained a RMSE of 5%–7% and a skill score with respect to the smart persistence from −8% to 33.6%. The one and two days ahead forecast achieved a RMSE of 7% and 7.5% and a skill score of 39.2% and 45.7%. The smoothing effect on cluster scale was also studied. It reduces the RMSE of power estimation of 33% and the RMSE of day-ahead forecast of 12% with respect to the mean single cluster value. Furthermore, a method to estimate the forecast error was also developed. It was based on an ensemble neural network model coupled with a probabilistic correction. It can provide a highly reliable computation of the prediction intervals. © 2017 Elsevier Ltd