An Internet of Things’ (IoT) connected society and system represents a tremendous paradigm shift. We present a framework for a decision-support system (DSS) that operates within the IoT ecosystem. The DSS leverages advanced analytics of electric smart meter (ESM) network communication-quality data to improve cost predictions for smart meter field operations and provide actionable decision recommendations regarding whether to send a technician to a customer location to resolve an ESM issue. The model is empirically evaluated using data sets from a commercial network. We demonstrate the efficiency of our approach with a complete Bayesian network prediction model and compare with three machine learning prediction model classifiers: 1) Naive Bayes; 2) random forest; and 3) decision tree. Results demonstrate that our approach generates statistically noteworthy estimations and that the DSS will improve the cost efficiency of ESM network operations and maintenance.

A Machine Learning Decision-Support System Improves the Internet of Things’ Smart Meter Operations

Coordinated charging of electric vehicles (EVs) improves the overall efficiency of the power grid as it avoids distribution system overloads, increases power quality, and decreases voltage fluctuations. Moreover, the coordinated charging supports flattening the load profile. Therefore, an effective coordination technique is crucial for the protection of the distribution grid and its components. The substantial power used through charging EVs has undeniable negative impacts on the power grid. Additionally, with the increasing use of EVs, an effective solution for the coordination of EVs charging, particularly when considering the anticipated proliferation of EV fast chargers, is imminently required. In this paper, different machine learning (ML) approaches are compared for the coordination of EVs charging. The ML models can predict the power to be used in EVs charging stations (EVCS). Due to its ability to use historical data to learn and identify patterns for making future decisions with minimal user intervention, ML has been utilized. ML models used in this paper are (1) Decision Tree (DT), (2) Random Forest (RF), (3) Support Vector Machine (SVM), (4) Naive Bayes (NB), (5) K-Nearest Neighbors (KNN), (6) Deep Neural Networks (DNN), and (7) Long Short-Term Memory (LSTM). These approaches are chosen as they are classifiers known to have the leading results for multiclass classification problems. The results found shed insight on the importance of the techniques used and their high potential in providing a reliable solution for the coordinated charging of EVs, thus improving the performance of the power grid, and reducing power losses and voltage fluctuations. The use of ML provides a less complex method to coordinate EVs, in comparison with conventional optimization techniques such as quadratic programming, and the use of ML is faster as it requires less computational power. LSTM provided the best results with an accuracy of 95% for predicting the most appropriate power rating (PR) for EVCS, followed by RF, DT, DNN, SVM, KNN, and NB. Additionally, LSTM was also the model with the smallest error rate, at a value of ±0.7%, followed by RF, DT, KNN, SVM, DNN, and NB. The results obtained from the LSTM model were similar to the results obtained from past literature using quadratic programming, with the increased speed and simplicity of ML.

https://www.mdpi.com/1996-1073/13/20/5429/pdf

Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers

In this paper, we focus on fault prediction in the smart distribution network. modified version of voted random forest algorithm (VRF) is proposed for enhancing the predicting accuracy of the fault...

A voted based random forests algorithm for smart grid distribution network faults prediction

There is a growing interest in applying machine learning methods on large amounts of data to solve complex problems, such as prediction of events and disturbances in the power system. This paper is a comparative study of the predictive performance of state-of-the-art supervised machine learning methods. The event prediction models are trained and validated using high-resolution power quality data from measuring instruments in the Norwegian power grid. The recorded event categories in the study were voltage dips, ground faults, rapid voltage changes and interruptions. Out of the tested machine learning methods, the Random Forest models indicated a better prediction performance, with an accuracy of 0.602. The results also indicated that rapid voltage changes (accuracy = 0.710) and voltage dips (accuracy = 0.601) are easiest to predict among the tested power quality events.

/pdf/comparative-study-of-event-prediction-in-power-grids-using-4bqog4uvsb.pdf

Comparative Study of Event Prediction in Power Grids using Supervised Machine Learning Methods

Recently, oil costs and environmental concerns have risen dramatically. Additionally, growing urbanization, urban mobility, and employment face several difficulties. Developing Electric Vehicles (EVs) is one of the crucial solutions. However, adopting EVs has remained difficult despite favorable consumer attitudes. This article analyzes public opinions and what they express on Twitter about EVs. We selected social media in this study because it is one of the most prevalent platforms for expressing opinions or viewpoints. Therefore, our goal is to calculate how frequently the occurrence of words is to understand the concerns and interests of the consumers about EVs. To achieve that, we utilized three Machine Learning (ML) models: Random Forest (RF), Decision Tree (DT), and Naïve Bais (NB). We evaluated each model in terms of accuracy and the Matthews Correlation Coefficient. The results demonstrate that all the models can achieve excellent accuracies, especially the DT model, which can give the best outcomes. 

Machine Learning-Based Social Media Text Analysis: Impact of the Rising Fuel Prices on Electric Vehicles

This paper proposes a system for the prediction of events in the smart grid. The system infers a label indicating if an event is going to occur in a future time window, in a specific asset, from data of events generated by grid assets and exogenous variables (e.g. weather data). The system design presented follows a sliding-window classification approach, bag-of-words event representation and makes use of random forests models. The systems performance is evaluated in an experimental case study, backed by real data, with the aim of predicting future interruptions in distribution transformers. Performance results indicate that the system is able to deal with highly imbalanced data and validate its adequacy in dealing with the approached problem, achieving up to 0.75 area under the receiver operating characteristic curve in testing.

Hugo A. Matos

Papers

Prediction of events in the smart grid: Interruptions in distribution transformers