Appliance-level Short-term Load Forecasting using Deep Neural Networks

The recently employed demand-response (DR) model enabled by the transformation of the traditional power grid to the SmartGrid (SG) allows energy providers to have a clearer understanding of the energy utilisation of each individual household within their administrative domain. Nonetheless, the rapid growth of IoT-based domestic appliances within each household in conjunction with the varying and hard-to-predict customer-specific energy requirements is regarded as a challenge with respect to accurately profiling and forecasting the day-to-day or week-to-week appliance-level power consumption demand. Such a forecast is considered essential in order to compose a granular and accurate aggregate-level power consumption forecast for a given household, identify faulty appliances, and assess potential security and resilience issues both from an end-user as well as from an energy provider perspective. Therefore, in this paper we investigate techniques that enable this and propose the applicability of Deep Neural Networks (DNNs) for short-term appliance-level power profiling and forecasting. We demonstrate their superiority over the past heavily used Support Vector Machines (SVMs) in terms of prediction accuracy and computational performance with experiments conducted over real appliance-level dataset gathered in four residential households

NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

Regional And Residential Short Term Electric Demand Forecast Using Deep Learning

For optimal power system operations, electric generation must follow load demand. The generation, transmission, and distribution utilities require load forecasting for planning and operating grid infrastructure efficiently, securely, and economically. This thesis work focuses on short-term load forecast (STLF), that concentrates on the time-interval from few hours to few days. An inaccurate short-term load forecast can result in higher cost of generating and delivering power. Hence, accurate short-term load forecasting is essential. Traditionally, short-term load forecasting of electrical demand is typically performed using linear regression, autoregressive integrated moving average models (ARIMA), and artificial neural networks (ANN). These conventional methods are limited in application for big datasets, and often their accuracy is a matter of concern. Recently, deep neural networks (DNNs) have emerged as a powerful tool for machine-learning problems, and known for real-time data processing, parallel computations, and ability to work with a large dataset with higher accuracy. DNNs have been shown to greatly outperform traditional methods in many disciplines, and they have revolutionized data analytics. Aspired from such a success of DNNs in machine learning problems, this thesis investigated the DNNs potential in electrical load forecasting application. Different DNN Types such as multilayer perception model (MLP) and recurrent neural networks (RNN) such as long short-term memory (LSTM), Gated recurrent Unit (GRU) and simple RNNs for different datasets were evaluated for accuracies. This thesis utilized the following data sets: 1) Iberian electric market dataset; 2) NREL residential home dataset; 3) AMPds smart-meter dataset; 4) UMass Smart Home datasets with varying time intervals or data duration for the validating the applicability of DNNs for short-term load forecasting. The Mean absolute percentage error (MAPE) evaluation indicates DNNs outperform conventional method for multiple datasets. In addition, a DNN based smart scheduling of appliances was also studied. This work evaluates MAPE accuracies of clustering-based forecast over non-clustered forecasts

Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review

Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for decomposing the total energy consumption profile into individual appliance load profiles within the household. It has multiple applications in demand-side management, energy consumption monitoring, and analysis. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads. It summarizes public databases for NILM evaluation and compares methods using standard performance metrics

Low-Frequency Load Identification using CNN-BiLSTM Attention Mechanism

Non-intrusive Load Monitoring (NILM) is an established technique for effective and cost-efficient electricity consumption management. The method is used to estimate appliance-level power consumption from aggregated power measurements. This paper presents a hybrid learning approach, consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory (BILSTM), featuring an integrated attention mechanism, all within the context of disaggregating low-frequency power data. While prior research has been mainly focused on high-frequency data disaggregation, our study takes a distinct direction by concentrating on low-frequency data. The proposed hybrid CNN-BILSTM model is adept at extracting both temporal (time-related) and spatial (location-related) features, allowing it to precisely identify energy consumption patterns at the appliance level. This accuracy is further enhanced by the attention mechanism, which aids the model in pinpointing crucial parts of the data for more precise event detection and load disaggregation. We conduct simulations using the existing low-frequency REDD dataset to assess our model performance. The results demonstrate that our proposed approach outperforms existing methods in terms of accuracy and computation time

Anomaly detection in quasi-periodic energy consumption data series: a comparison of algorithms

The diffusion of domotics solutions and of smart appliances and meters enables the monitoring of energy consumption at a very fine level and the development of forecasting and diagnostic applications. Anomaly detection (AD) in energy consumption data streams helps identify data points or intervals in which the behavior of an appliance deviates from normality and may prevent energy losses and break downs. Many statistical and learning approaches have been applied to the task, but the need remains of comparing their performances with data sets of different characteristics. This paper focuses on anomaly detection on quasi-periodic energy consumption data series and contrasts 12 statistical and machine learning algorithms tested in 144 different configurations on 3 data sets containing the power consumption signals of fridges. The assessment also evaluates the impact of the length of the series used for training and of the size of the sliding window employed to detect the anomalies. The generalization ability of the top five methods is also evaluated by applying them to an appliance different from that used for training. The results show that classical machine learning methods (Isolation Forest, One-Class SVM and Local Outlier Factor) outperform the best neural methods (GRU/LSTM autoencoder and multistep methods) and generalize better when applied to detect the anomalies of an appliance different from the one used for training

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

A neural ordinary differential equations based approach for demand forecasting within power grid digital twins

Over the past few years, deep learning (DL) based electricity demand forecasting has received considerable attention amongst mathematicians, engineers and data scientists working within the smart grid domain. To this end, deep learning architectures such as deep neural networks (DNN), deep belief networks (DBN) and recurrent neural networks (RNN) have been successfully applied to forecast the generation and consumption of a wide range of energy vectors. In this work, we show preliminary results for a residential load demand forecasting solution which is realized within the framework of power grid digital twin. To this end, a novel class of deep neural networks is adopted wherein the output of the network is efficiently computed via a black-box ordinary differential equation (ODE) solver. We introduce the readers to the main concepts behind this method followed by a real-world, data driven computational benchmark test case designed to study the numerical effectiveness of the proposed approach. Initial results suggest that the ODE based solutions yield acceptable levels of accuracy for wide range of prediction horizons. We conclude that the method could prove as a valuable tool to develop forecasting models within an electrical digital twin (EDT) framework, where, in addition to accurate prediction models, a time horizon independent, computationally scalable and compact model is often desired.This research that contributed to this paper was funded by the EPSRC/Innovate UK Centre for Smart Infrastructure and Construction (CSIC) and Centre for Digital Built Britain (CDBB) at the University of Cambridge

Power Management of Nanogrid Cluster with P2P Electricity Trading Based on Future Trends of Load Demand and PV Power Production

This paper presents the power management of the nanogrid clusters assisted by a novel peer-to-peer(P2P) electricity trading. In our work, unbalance of power consumption among clusters is mitigated by the proposed P2P trading method. For power management of individual clusters, multi-objective optimization simultaneously minimizing total power consumption, portion of grid power consumption, and total delay incurred by scheduling is attempted. A renewable power source photovoltaic(PV) system is adopted for each cluster as a secondary source. The temporal surplus of self-supply PV power of a cluster can be sold through P2P trading to another cluster (s) experiencing temporal power shortage. The cluster in temporal shortage of electric power buys the PV power to reduce peak load and total delay. In P2P trading, a cooperative game model is used for buyers and sellers to maximize their welfare. To increase P2P trading efficiency, future trends of load demand and PV power production are considered for power management of each cluster to resolve instantaneous unbalance between load demand and PV power production. To this end, a gated recurrent unit network is used to forecast future load demand and future PV power production. Simulations verify the effectiveness of the proposed P2P trading for nanogrid clusters.Comment: This article is submitted for publication in Sustainable Cities and Societ