Azure Machine Learning with Power BI SandDance Visualization


Power BI is the self-service BI engine of Microsoft, this service provides ability for business users to work with data with easier tools and be able to do changes or build their own models on top of one or more data sources. This is a full day of Power BI with many live demos. Training starts with Power BI and its components (Power Query, Power Pivot, Power View, Power Map, and Power Q&A). Course continues with real world demos of fetching data and transforming it with Power Query, loading and Modeling in Power Pivot and DAX, Visualizing it in Power BI Desktop and enhanced usage of Power Q&A. Many tips and best practices for using each component will be explained as well.

Use Power BI API with service principal (Preview) Machine Learning Analytikus United States


Service principal is a local representation of your AAD application for use in a specific tenant and will allow you to access resources or perform operations using Power BI API without the need for a user to sign in or have a Power BI Pro license. For customers using Power BI Embedded it can significantly reduce other limitations and friction.

Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells Machine Learning

We propose a closed-loop power control algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an indoor environment served by small cells. The main contributions of our paper are: 1) proposing closed-loop power control for downlink VoLTE (or any packetized voice bearer), 2) deriving an upper bound of the loss in VoLTE downlink signal to noise plus interference ratio which the closed-loop power control has to overcome, 3) employing reinforcement learning to perform closed-loop power control, and 4) showing that this closed-loop power control method can improve the quality of VoLTE in a realistic network setup. Our simulation results have shown that our proposed algorithm significantly improved both voice retainability and mean opinion score as a result of maintaining the effective downlink signal to interference plus noise ratio against adverse network operational issues and faults.

Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulation Machine Learning

Electronic power inverters are capable of quickly delivering reactive power to maintain customer voltages within operating tolerances and to reduce system losses in distribution grids. This paper proposes a systematic and data-driven approach to determine reactive power inverter output as a function of local measurements in a manner that obtains near optimal results. First, we use a network model and historic load and generation data and do optimal power flow to compute globally optimal reactive power injections for all controllable inverters in the network. Subsequently, we use regression to find a function for each inverter that maps its local historical data to an approximation of its optimal reactive power injection. The resulting functions then serve as decentralized controllers in the participating inverters to predict the optimal injection based on a new local measurements. The method achieves near-optimal results when performing voltage- and capacity-constrained loss minimization and voltage flattening, and allows for an efficient volt-VAR optimization (VVO) scheme in which legacy control equipment collaborates with existing inverters to facilitate safe operation of distribution networks with higher levels of distributed generation.

A Deep Generative Model for Graphs: Supervised Subset Selection to Create Diverse Realistic Graphs with Applications to Power Networks Synthesis Machine Learning

Creating and modeling real-world graphs is a crucial problem in various applications of engineering, biology, and social sciences; however, learning the distributions of nodes/edges and sampling from them to generate realistic graphs is still challenging. Moreover, generating a diverse set of synthetic graphs that all imitate a real network is not addressed. In this paper, the novel problem of creating diverse synthetic graphs is solved. First, we devise the deep supervised subset selection (DeepS3) algorithm; Given a ground-truth set of data points, DeepS3 selects a diverse subset of all items (i.e. data points) that best represent the items in the ground-truth set. Furthermore, we propose the deep graph representation recurrent network (GRRN) as a novel generative model that learns a probabilistic representation of a real weighted graph. Training the GRRN, we generate a large set of synthetic graphs that are likely to follow the same features and adjacency patterns as the original one. Incorporating GRRN with DeepS3, we select a diverse subset of generated graphs that best represent the behaviors of the real graph (i.e. our ground-truth). We apply our model to the novel problem of power grid synthesis, where a synthetic power network is created with the same physical/geometric properties as a real power system without revealing the real locations of the substations (nodes) and the lines (edges), since such data is confidential. Experiments on the Synthetic Power Grid Data Set show accurate synthetic networks that follow similar structural and spatial properties as the real power grid.