Potential of Ensemble Copula Coupling for Wind Power Forecasting

With the share of renewable energy sources in the energy system increasing,accurate wind power forecasts are required to ensure a balanced supply anddemand. Wind power is, however, highly dependent on the chaotic weathersystem and other stochastic features. Therefore, probabilistic wind powerforecasts are essential to capture uncertainty in the model parameters and inputfeatures. The weather and wind power forecasts are generally post-processedto eliminate some of the systematic biases in the model and calibrate it topast observations. While this is successfully done for wind power forecasts,the approaches used often ignore the inherent correlations among the weathervariables. The present paper, therefore, extends the previous post-processingstrategies by including Ensemble Copula Coupling (ECC) to restore the de-pendency structures between variables and investigates, whether including thedependency structures changes the optimal post-processing strategy. We findthat the optimal post-processing strategy does not change when including ECCand ECC does not improve the forecast accuracy when the dependency struc-tures are weak. We, therefore, suggest investigating the dependency structuresbefore choosing a post-processing strategy

Customized Uncertainty Quantification of Parking Duration Predictions for EV Smart Charging

As Electric Vehicle (EV) demand increases, so does the demand for efficient Smart Charging (SC) applications. However, SC is only acceptable if the EV user’s mobility requirements and risk preferences are fulfilled, i.e. their respective EV has enough charge to make their planned journey. To fulfill these requirements and risk preferences, the SC application must consider the predicted parking duration at a given location and the uncertainty associated with this prediction. However, certain regions of uncertainty are more critical than others for user-centric SC applications, and therefore, such uncertainty must be explicitly quantified. Therefore, the present paper presents multiple approaches to customize the uncertainty quantification of parking duration predictions specifically for EV user-centric SC applications. We decompose parking duration prediction errors into a critical component which results in undercharging, and a non-critical component. Furthermore, we derive quantile-based security levels that can minimize the probability of a critical error given a user’s risk preferences. We evaluate our customized uncertainty quantification with four different probabilistic prediction models on an openly available semi-synthetic mobility data set and a data set consisting of real EV trips. We show that our customized uncertainty quantification can regulate critical errors, even in challenging real-world data with high fluctuation and uncertainty

Evaluating Ensemble Post-Processing for Wind Power Forecasts

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biased and underdispersed, meteorologists post-process the ensembles. This post-processing can successfully correct the biases in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post-processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post-processing method and evaluate four possible strategies: only using the raw ensembles without post-processing, a one-step strategy where only the weather ensembles are post-processed, a one-step strategy where we only post-process the power ensembles, and a two-step strategy where we post-process both the weather and power ensembles. Results show that post-processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness, whilst only post-processing the weather ensembles does not necessarily lead to increased forecast performance

High-resolution real-world electricity data from three microgrids in the global south

Microgrids are a promising solution for providing renewable electricity access to rural populations in the Global South. To ensure such renewable microgrids are affordable, careful planning and dimensioning are required. High-resolution data on electricity generation and consumption is necessary for optimal design. Unfortunately, real-world electricity data for microgrids in the Global South is scarce, and the limited data that is available has a low temporal resolution. Therefore, in this paper, we introduce a unique high-resolution real-world electricity data set from three micro-grids in the Democratic Republic of the Congo, Rwanda, and Haiti. The data has a temporal resolution of up to five seconds and focuses on microgrids with renewable generation from either hydropower or photovoltaic systems. Furthermore, we include data from both residential and industrial microgrids. We describe the recorded data and highlight the advantages of the high resolution. We demonstrate how this resolution offers insight into consumption patterns and enables the analysis of grid voltage and frequency, which is highly relevant for the planning and dimensioning of affordable renewable microgrids in the Global South

High-Resolution Real-World Electricity Data from Three Microgrids in the Global South

Microgrids are a promising solution for providing renewable electricity access to rural populations in the Global South. To ensure such renewable microgrids are affordable, careful planning and dimensioning are required. High-resolution data on electricity generation and consumption is necessary for optimal design. Unfortunately, real-world electricity data for microgrids in the Global South is scarce, and the little data that is available has a low temporal resolution. Therefore, in this paper, we introduce a unique highresolution real-world electricity data set from three microgrids in the Democratic Republic of the Congo, Rwanda, and Haiti. The data has a temporal resolution of up to five seconds and focuses on microgrids with renewable generation from either hydropower or photovoltaic systems. Furthermore, we include data from both residential and industrial microgrids. We describe the recorded data and highlight the advantages of the high resolution. We demonstrate how this resolution offers insight into consumption patterns and enables the analysis of grid voltage and frequency, which is highly relevant for the planning and dimensioning of affordable renewable microgrids in the Global South

Review of automated time series forecasting pipelines

Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting