Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble

One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an unprecedented advantage in realizing an optimised supply of thermal energies through proactive techniques such as load forecasting. In this paper, we propose a forecasting framework for heat demand based on neural networks where the time series are encoded as scalograms equipped with the capacity of embedding exogenous variables such as weather, and holiday/non-holiday. Subsequently, CNNs are utilized to predict the heat load multi-step ahead. Finally, the proposed framework is compared with other state-of-the-art methods, such as SARIMAX and LSTM. The quantitative results from retrospective experiments show that the proposed framework consistently outperforms the state-of-the-art baseline method with real-world data acquired from Denmark. A minimal mean error of 7.54% for MAPE and 417kW for RMSE is achieved with the proposed framework in comparison to all other methods.Comment: https://www.climatechange.ai/papers/neurips2022/4

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery

Intraoperative brain shift during neurosurgical procedures is a well-known phenomenon caused by gravity, tissue manipulation, tumor size, loss of cerebrospinal fluid (CSF), and use of medication. For the use of image-guided systems, this phenomenon greatly affects the accuracy of the guidance. During the last several decades, researchers have investigated how to overcome this problem. The purpose of this paper is to present a review of publications concerning different aspects of intraoperative brain shift especially in a tumor resection surgery such as intraoperative imaging systems, quantification, measurement, modeling, and registration techniques. Clinical experience of using intraoperative imaging modalities, details about registration, and modeling methods in connection with brain shift in tumor resection surgery are the focuses of this review. In total, 126 papers regarding this topic are analyzed in a comprehensive summary and are categorized according to fourteen criteria. The result of the categorization is presented in an interactive web tool. The consequences from the categorization and trends in the future are discussed at the end of this work

Implications of Experiment Set-Ups for Residential Water End-Use Classification

With an increasing need for secured water supply, a better understanding of the water consumption behavior is beneficial. This can be achieved through end-use classification, i.e., identifying end-uses such as toilets, showers or dishwashers from water consumption data. Previously, both supervised and unsupervised machine learning (ML) techniques are employed, demonstrating accurate classification results on particular datasets. However, a comprehensive comparison of ML techniques on a common dataset is still missing. Hence, in this study, we are aiming at a quantitative evaluation of various ML techniques on a common dataset. For this purpose, a stochastic water consumption simulation tool with high capability to model the real-world water consumption pattern is applied to generate residential data. Subsequently, unsupervised clustering methods, such as dynamic time warping, k-means, DBSCAN, OPTICS and Hough transform, are compared to supervised methods based on SVM. The quantitative results demonstrate that supervised approaches are capable to classify common residential end-uses (toilet, shower, faucet, dishwasher, washing machine, bathtub and mixed water-uses) with accuracies up to 0.99, whereas unsupervised methods fail to detect those consumption categories. In conclusion, clustering techniques alone are not suitable to separate end-use categories fully automatically. Hence, accurate labels are essential for the end-use classification of water events, where crowdsourcing and citizen science approaches pose feasible solutions for this purpose