Real-time fMRI feedback training may improve chronic tinnitus

Objectives: Tinnitus consists of a more or less constant aversive tone or noise and is associated with excess auditory activation. Transient distortion of this activation (repetitive transcranial magnetic stimulation, rTMS) may improve tinnitus. Recently proposed operant training in real-time functional magnetic resonance imaging (rtfMRI) neurofeedback allows voluntary modification of specific circumscribed neuronal activations. Combining these observations, we investigated whether patients suffering from tinnitus can (1) learn to voluntarily reduce activation of the auditory system by rtfMRI neurofeedback and whether (2) successful learning improves tinnitus symptoms. Methods: Six participants with chronic tinnitus were included. First, location of the individual auditory cortex was determined in a standard fMRI auditory block-design localizer. Then, participants were trained to voluntarily reduce the auditory activation (rtfMRI) with visual biofeedback of the current auditory activation. Results: Auditory activation significantly decreased after rtfMRI neurofeedback. This reduced the subjective tinnitus in two of six participants. Conclusion: These preliminary results suggest that tinnitus patients learn to voluntarily reduce spatially specific auditory activations by rtfMRI neurofeedback and that this may reduce tinnitus symptoms. Optimized training protocols (frequency, duration, etc.) may further improve the result

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

AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models

Accurate PhotoVoltaic (PV) power generation forecasting is vital for the efficient operation of Smart Grids. The automated design of such accurate forecasting models for individual PV plants includes two challenges: First, information about the PV mounting configuration (i.e. inclination and azimuth angles) is often missing. Second, for new PV plants, the amount of historical data available to train a forecasting model is limited (cold-start problem). We address these two challenges by proposing a new method for day-ahead PV power generation forecasts called AutoPV. AutoPV is a weighted ensemble of forecasting models that represent different PV mounting configurations. This representation is achieved by pre-training each forecasting model on a separate PV plant and by scaling the model's output with the peak power rating of the corresponding PV plant. To tackle the cold-start problem, we initially weight each forecasting model in the ensemble equally. To tackle the problem of missing information about the PV mounting configuration, we use new data that become available during operation to adapt the ensemble weights to minimize the forecasting error. AutoPV is advantageous as the unknown PV mounting configuration is implicitly reflected in the ensemble weights, and only the PV plant's peak power rating is required to re-scale the ensemble's output. AutoPV also allows to represent PV plants with panels distributed on different roofs with varying alignments, as these mounting configurations can be reflected proportionally in the weighting. Additionally, the required computing memory is decoupled when scaling AutoPV to hundreds of PV plants, which is beneficial in Smart Grids with limited computing capabilities. For a real-world data set with 11 PV plants, the accuracy of AutoPV is comparable to a model trained on two years of data and outperforms an incrementally trained model

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

Using weather data in energy time series forecasting: the benefit of input data transformations

Renewable energy systems depend on the weather, and weather information, thus, plays a crucial role in forecasting time series within such renewable energy systems. However, while weather data are commonly used to improve forecast accuracy, it still has to be determined in which input shape this weather data benefits the forecasting models the most. In the present paper, we investigate how transformations for weather data inputs, i. e., station-based and grid-based weather data, influence the accuracy of energy time series forecasts. The selected weather data transformations are based on statistical features, dimensionality reduction, clustering, autoencoders, and interpolation. We evaluate the performance of these weather data transformations when forecasting three energy time series: electrical demand, solar power, and wind power. Additionally, we compare the best-performing weather data transformations for station-based and grid-based weather data. We show that transforming station-based or grid-based weather data improves the forecast accuracy compared to using the raw weather data between 3.7 and 5.2%, depending on the target energy time series, where statistical and dimensionality reduction data transformations are among the best

Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms

The zip archive contains real images showing macrophages. (ZIP 28979 kb

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

Evaluation of Transformer Architectures for Electrical Load Time-Series Forecasting

Accurate forecasts of the electrical load are needed to stabilize the electrical grid and maximize the use of renewable energies. Many good forecasting methods exist, including neural networks, and we compare them to the recently developed Transformers, which are the state-of-the-art machine learning technique for many sequence-related tasks. We apply different types of Transformers, namely the Time-Series Transformer, the Convolutional Self-Attention Transformer and the Informer, to electrical load data from Baden-Württemberg. Our results show that the Transformes give up to 11% better forecasts than multi-layer perceptrons for long prediction horizons. Furthermore, we analyze the Transformers’ attention scores to get insights into the model