Load Disaggregation Using One-Directional Convolutional Stacked Long Short-Term Memory Recurrent Neural Network

Reliable information about the active loads in the energy system allows for effective and optimized energy management. An important aspect of intelligent energy monitoring system is load disaggregation. The proliferation of direct current (dc) loads has spurred the increasing research interest in extra low voltage (ELV) dc grids. Artificial intelligence, such as deep learning algorithms of stacked recurrent neural network (RNN), improved results on a variety of regression and classification tasks. This paper proposes a 1-D convolutional stacked long short-term memory RNN technique for the bottom-up approach in load disaggregation using single sensor multiple loads ELV dc picogrids. This eliminates the requirement for communication and intelligence on every load in the grid. The proposed technique was applied on two different dc picogrids to test the algorithm's robustness. The proposed technique produced excellent result of over 98% accuracy for smart loads and over 99% accuracy for dumb loads in ELV dc picogrid

IoT Load Classification and Anomaly Warning in ELV DC Pico-grids using Hierarchical Extended k-Nearest Neighbors

The remote monitoring of electrical systems has progressed beyond the need of knowing how much energy is consumed. As the maintenance procedure has evolved from reactive to preventive to predictive, there is a growing demand to know what appliances reside in the circuit (classification) and a need to know whether any appliance requires attention and maintenance (anomaly warning). Targeting at the increasing penetration of dc appliances and equipment in households and offices, the described low-cost solution consists of multiple distributed slave meters with a single master computer for extra low voltage dc pico-grids. The slave meter acquires the current and voltage waveform from the cable of interest, conditions the data and extracts four features per window block that are sent remotely to the master computer. The proposed solution uses a hierarchical extended k-nearest neighbors (HE-kNN) technique that exploits the use of distance in kNN algorithm and considers a window block instead of individual data point for classification and anomaly warning to trigger the attention of the user. This solution can be used as an ad hoc standalone investigation of suspicious circuit or further expanded to several circuits in a building or vicinity to monitor the network. The solution can also be implemented as part of an Internet of Things application. This paper presents the successful implementation of HE-kNN technique in three different circuits: lightings, air-conditioning and multiple load dc pico-grids with accuracy of over 93%. Its performance is superior over other anomaly warning techniques with the same set of data

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

A Heterogeneous Communications Network for Smart Grid by Using the Cost Functions

Smart Grids (SG) is an intelligent power grid in which the different SG node types with different communication requirements communicates different types of information with Control Stations (CS). Radio Access Technologies (RATs) due to its advantages are considered as the main access method to be used in order to have bidirectional data transferring between different node types and CS. Besides, spectrum is a rare source and its demand is increasing significantly. Elaborating a heterogeneous in order to fulfill different SG node types communication requirements effectively, is a challenging issue. To find a method to define desirability value of different RAT to support certain node types based on fitness degree between RAT communication characteristics and node type communication requirements is an appropriate solution. This method is implemented by using a comprehensive Cost Function (CF) including a communication CF (CCF) in combination with Energy CF (ECF). The Key Point Indicators which are used in the CCF are SG node type communication requirements. The existing trade of between Eb/N0 and spectral efficiency is considered as ECF. Based on the achieved CCF and ECF and their tradeoffs, SG node types are assigned to different RATs. The proposed assigning method is sensitive to the SG node types densities. The numerical results are achieved by using MATLAB simulation. The other different outcomes of the research output such as cognitive radio in SG and collectors effect number on data aggregation are discussed as well