Deep Learning Based Consumer Classification for Smart Grid (original) (raw)
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International Journal of Applied Power Engineering (IJAPE), 2024
A smart grid is a cutting-edge energy system designed to take over oldfashioned energy infrastructure in the twenty-first century. With comprehensive communication and computation capabilities, its primary objective is to increase energy distribution's dependability and efficiency while minimizing unfavorable effects. A number of approaches are needed for effective analysis and well-informed decision-making due to the massive infrastructure and integrated network of communications of the smart grid. In this study, we examine the architectural elements of the smart grid as well as the uses and methods using machine learning (ML) and deep learning (DL) with regard to the smart grid. We also clarify present research limitations and propose future directions for machine learning-driven data analytics. In order to improve the stability, reliability, security, efficiency, and responsiveness of the smart grid, this paper examines the implementation of several machine learning methodologies. This paper also covers some of the difficulties in putting machine learning solutions for smart grids into practice.
An Insight of Deep Learning Based Demand Forecasting in Smart Grids
Sensors
Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today’s demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeho...
2020
Today, there is a lot of enthusiasm to fulfil global energy needs from alternative energy resources. Due to the increasing demand for electricity, the traditional electricity market relies on decisions to plan electricity systems, and to generate and distribute electricity to their consumers to balance demand and supply. The peak demands of electricity highly affect these decisions and often cause system failure and shortage of electricity. By predicting energy requirements, these peak demands and the uncertainties in human behaviour in households are optimised to balance the load through various demand response programmes. A smart grid ecosystem requires intelligent Home Energy Management Systems (HEMSs) to profile highly non-stationary and non-linear measurements and conduct correlations of such measurements with diverse inputs (e.g. environmental factors) in order to improve the end-user experience, as well as to aid the overall demand-response optimisation process. The huge amou...
Journal of Intelligent Systems and Internet of Things (JISIoT), 2021
In the 21 st century, the Smart Grid (SG), also known as the next-generation power grid, arose as a substitute for inefficient power systems, ensuring a reliable and efficient power supply. It is projected to improve the reliability and efficiency of energy distribution while having minimal side effects because it is coupled with modern communication and computation capabilities. The huge infrastructure it possesses, as well as the system's underlying communication network, has resulted in a large number of data that necessitates the use of diverse approaches for proper analysis and decision making. When it comes to analyzing this huge amount of data and generating significant insights from it, big data analytics, machine learning (ML), and deep learning (DL), all play a key role. These insights are useful for anomaly detection, fraud detection, price confirmation, fault detection, monitoring energy consumption, and so on. Hence constant and continuous data analysis is an essential part, of the modern smart grid, for its existence. Inspired by providing a reliable and efficient energy distribution, this paper explores and surveys the smart grid architectural elements, ML and DL based applications, and approaches in the context of SG. In addition in terms of ML and DL based data analytics, this paper highlights the limitations of the current research and, highlights future directions as well.
IEEE Access
Different aggregation levels of the electric grid's big data can be helpful to develop highly accurate deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Whilst different models are proposed for STLF, they are based on small historical datasets and are not scalable to process large amounts of big data as energy consumption data grow exponentially in large electric distribution networks. This paper proposes a novel hybrid clustering-based deep learning approach for STLF at the distribution transformers' level with enhanced scalability. It investigates the gain in training time and the performance in terms of accuracy when clustering-based deep learning modeling is employed for STLF. A k-Medoid based algorithm is employed for clustering whereas the forecasting models are generated for different clusters of load profiles. The clustering of the distribution transformers is based on the similarity in energy consumption profile. This approach reduces the training time since it minimizes the number of models required for many distribution transformers. The developed deep neural network consists of six layers and employs Adam optimization using the TensorFlow framework. The STLF is a day-ahead hourly horizon forecasting. The accuracy of the proposed modeling is tested on a 1,000-transformer substation subset of the Spanish distribution electrical network data containing more than 24 million load records. The results reveal that the proposed model has superior performance when compared to the state-of-the-art STLF methodologies. The proposed approach delivers an improvement of around 44% in training time while maintaining accuracy using single-core processing as compared to non-clustering models. INDEX TERMS Deep neural networks, distribution transformers, k-medoids clustering, machine learning, short-term load forecasting.
Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data
Sensors
The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clustering-based analysis of energy consumption to categorize the consumers’ electricity usage into different levels. First, a deep autoencoder that transfers the low-dimensional energy consumption data to high-level representations was trained. Second, the high-level representations were fed into an adaptive self-organizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting...
IEEE Access
Demand response modelling have paved an important role in smart grid at a greater perspective. DR analysis exhibits the analysis of scheduling of appliances for an optimal strategy at the user's side with an effective pricing scheme. In this proposed work, the entire model is done in three different steps. The first step develops strategy patterns for the users considering integration of renewable energy and effective demand response analysis is done. The second step in the process exhibits the learning process of the consumers using Robust Adversarial Reinforcement Learning for privacy process among the users. The third step develops optimal strategy plan for the users for maintaining privacy among the users. Considering the uncertainties of the user's behavioral patterns, typical pricing schemes are involved with integration of renewable energy at the user' side so that an optimal strategy is obtained. The optimal strategy for scheduling the appliances solving privacy issues and considering renewable energy at user' side is done using Robust Adversarial Reinforcement learning and Gradient Based Nikaido-Isoda Function which gives an optimal accuracy. The results of the proposed work exhibit optimal strategy plan for the users developing proper learning paradigm. The effectiveness of the proposed work with mathematical modelling are validated using real time data and shows the demand response strategy plan with proper learning access model. The results obtained among the set of strategy develops 80 % of the patterns created with the learning paradigm moves with optimal DR scheduling patterns. This work embarks the best learning DR pattern created for the future set of consumers following the strategy so privacy among the users can be maintained effectively.
Journal of Network and Computer Applications, 2017
Smart management of power consumption for green living is important for sustainable development. Existing approaches could not provide a complete solution for both smart monitoring of electricity consumption, and also intelligent processing of the collected data effectively. This paper presents a cloud-based intelligent power distribution service architecture, where an intelligent electricity box (IEB) is designed using Zigbee and Raspberry Pi, and a standard MQTT (Message Queuing Telemetry Transport) protocol is used to transfer monitored data to the backend Cloud computing infrastructure using open source software packages. The IEB provides cloud services of real-time electricity information checking, power consumption monitoring, and remote control of switches. The current and historical data are stored in HBase and analyzed using Long Short Term Memory (LSTM). Evaluations and practical usage show that our proposed solution is very efficient in terms of availability, performance, and the deep learning based approach has better prediction accuracy than that of both classical SVR based approach and the latest XGBoost approach.
Electricity-theft detection in smart grids based on deep learning
Bulletin of Electrical Engineering and Informatics, 2021
Electricity theft is a major concern for utilities. The smart grid (SG) infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning, and deep learning techniques can accurately identify electricity theft users. A convolutional neural network (CNN) model for automatic electricity theft detection is presented. This work considers experimentation to find the best configuration of the sequential model (SM) for classifying and identifying electricity theft. The best performance has been obtained in two layers with the first layer consists of 128 nodes and the second layer is 64 nodes. The accuracy reached up to 0.92. This enables the design of high-performance electricity signal classifiers that can be used in several applications. Designing electricity signals classifiers has been achieved using a CNN and the data extracted from the electricity consumption dataset using an SM. In addition, the blue monkey (BM) algorithm is used to reduce the features in the dataset. In this respect, the focusing of this work is to reduce the features in the dataset to obtain high-performance electricity signals classifier models.
Improving Power Generation Efficiency using Deep Neural Networks
arXiv (Cornell University), 2016
Recently there has been significant research on power generation, distribution and transmission efficiency especially in the case of renewable resources. The main objective is reduction of energy losses and this requires improvements on data acquisition and analysis. In this paper we address these concerns by using consumers' electrical smart meter readings to estimate network loading and this information can then be used for better capacity planning. We compare Deep Neural Network (DNN) methods with traditional methods for load forecasting. Our results indicate that DNN methods outperform most traditional methods. This comes at the cost of additional computational complexity but this can be addressed with the use of cloud resources. We also illustrate how these results can be used to better support dynamic pricing.