Smart electricity meters have been replacing conventional meters worldwide, enabling
automated collection of fine-grained (eg, every 15 minutes or hourly) consumption data. A …

This study presents a novel feature-engineered–natural gradient descent ensemble-
boosting (NGBoost) machine-learning framework for detecting fraud in power consumption …

In this paper, we investigate a critical problem in smart meter data mining: computing
electricity consumption profiles. We present a simple, interpretable and practical profiling …

To effectively predict generation of distributed photovoltaic (PV) systems, three parameters
are critical: irradiance, ambient temperature, and module temperature. However, their …

Forecasting time series data is an integral component for management, planning and
decision making. Following the Big Data trend, large amounts of time series data are …

Despite a rapid increase of public interest for electric mobility, several factors still impede
Battery Electric Vehicles'(BEVs) acceptance. These factors include their limited range and …

This paper presents a thorough analysis of 30-minute data sets of KSA residential digital
meters to identify all possible discrepancies in the data sets and devise statistical techniques …

Automatic and remote reading systems of energy meters are spreading more each day.
However, electricity meter data sometimes bear missing elements and outliers, due to …

Forecasting time series data is an integral component for management, planning and
decision making. Following the Big Data trend, large amounts of time series data are …

Smart electricity grids, which include renewable energy sources such as solar and wind and
allow information sharing among producers and consumers, are beginning to replace …