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

A Scoping Review of Energy Load Disaggregation

Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management and promoting electricity-saving behavior through increased consumer awareness. However, the field currently lacks a comprehensive overview. To address this gap, this paper con-ducts a scoping review of load disaggregation domains, data types, and methods, by assessing 72 full-text journal articles. The findings reveal that domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed. The majority of research uses relatively low-frequency data, sampled between 1 and 60 seconds. A wide variety of methods are used, and artificial neural networks are the most common, followed by optimization strategies, Hidden Markov Models, and Graph Signal Processing approaches

Hierarchical feature extraction from spatiotemporal data for cyber-physical system analytics

With the advent of ubiquitous sensing, robust communication and advanced computation, data-driven modeling is increasingly becoming popular for many engineering problems. Eliminating difficulties of physics-based modeling, avoiding simplifying assumptions and ad hoc empirical models are significant among many advantages of data-driven approaches, especially for large-scale complex systems. While classical statistics and signal processing algorithms have been widely used by the engineering community, advanced machine learning techniques have not been sufficiently explored in this regard. This study summarizes various categories of machine learning tools that have been applied or may be a candidate for addressing engineering problems. While there are increasing number of machine learning algorithms, the main steps involved in applying such techniques to the problems consist in: data collection and pre-processing, feature extraction, model training and inference for decision-making. To support decision-making processes in many applications, hierarchical feature extraction is key. Among various feature extraction principles, recent studies emphasize hierarchical approaches of extracting salient features that is carried out at multiple abstraction levels from data. In this context, the focus of the dissertation is towards developing hierarchical feature extraction algorithms within the framework of machine learning in order to solve challenging cyber-physical problems in various domains such as electromechanical systems and agricultural systems. Furthermore, the feature extraction techniques are described using the spatial, temporal and spatiotemporal data types collected from the systems. The wide applicability of such features in solving some selected real-life domain problems are demonstrated throughout this study

Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM

Machine Learning for Human Activity Detection in Smart Homes

Recognizing human activities in domestic environments from audio and active power consumption sensors is a challenging task since on the one hand, environmental sound signals are multi-source, heterogeneous, and varying in time and on the other hand, the active power consumption varies significantly for similar type electrical appliances. Many systems have been proposed to process environmental sound signals for event detection in ambient assisted living applications. Typically, these systems use feature extraction, selection, and classification. However, despite major advances, several important questions remain unanswered, especially in real-world settings. A part of this thesis contributes to the body of knowledge in the field by addressing the following problems for ambient sounds recorded in various real-world kitchen environments: 1) which features, and which classifiers are most suitable in the presence of background noise? 2) what is the effect of signal duration on recognition accuracy? 3) how do the SNR and the distance between the microphone and the audio source affect the recognition accuracy in an environment in which the system was not trained? We show that for systems that use traditional classifiers, it is beneficial to combine gammatone frequency cepstral coefficients and discrete wavelet transform coefficients and to use a gradient boosting classifier. For systems based on deep learning, we consider 1D and 2D CNN using mel-spectrogram energies and mel-spectrograms images, as inputs, respectively and show that the 2D CNN outperforms the 1D CNN. We obtained competitive classification results for two such systems and validated the performance of our algorithms on public datasets (Google Brain/TensorFlow Speech Recognition Challenge and the 2017 Detection and Classification of Acoustic Scenes and Events Challenge). Regarding the problem of the energy-based human activity recognition in a household environment, machine learning techniques to infer the state of household appliances from their energy consumption data are applied and rule-based scenarios that exploit these states to detect human activity are used. Since most activities within a house are related with the operation of an electrical appliance, this unimodal approach has a significant advantage using inexpensive smart plugs and smart meters for each appliance. This part of the thesis proposes the use of unobtrusive and easy-install tools (smart plugs) for data collection and a decision engine that combines energy signal classification using dominant classifiers (compared in advanced with grid search) and a probabilistic measure for appliance usage. It helps preserving the privacy of the resident, since all the activities are stored in a local database. DNNs received great research interest in the field of computer vision. In this thesis we adapted different architectures for the problem of human activity recognition. We analyze the quality of the extracted features, and more specifically how model architectures and parameters affect the ability of the automatically extracted features from DNNs to separate activity classes in the final feature space. Additionally, the architectures that we applied for our main problem were also applied to text classification in which we consider the input text as an image and apply 2D CNNs to learn the local and global semantics of the sentences from the variations of the visual patterns of words. This work helps as a first step of creating a dialogue agent that would not require any natural language preprocessing. Finally, since in many domestic environments human speech is present with other environmental sounds, we developed a Convolutional Recurrent Neural Network, to separate the sound sources and applied novel post-processing filters, in order to have an end-to-end noise robust system. Our algorithm ranked first in the Apollo-11 Fearless Steps Challenge.Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 676157, project ACROSSIN

Computational intelligence in extra low voltage direct currrent pico-grids

Ph. D. ThesisThe modern power system has gone through a lot of changes over the past few years. It is no longer about providing one-way power from sources to various loads. Power monitoring and management have become an increasingly essential task with the growing trend to provide users more information about the status of the loads within their energy consumption so that they can make an informed decision to reduce usage and cost or request desired maintenance. Computational intelligence has been successfully implemented in the electrical power systems to aid the user, but these research studies about this are generally conducted on the conventional alternative current (AC) macro-grids. Until now, little work has been done on direct current (DC) and the focus on smaller DC grids has been even less. In recent years, the evolution of electrical power system has seen the proliferation of direct current (DC) appliances and equipment such as buildings, households and office loads. This number keeps increasing with the advancement in technology and consumer lifestyles changes. Given that DC power supplies are getting more popular in the form of photovoltaic panels and batteries, it is possible for Extra Low Voltage (ELV) DC households or office pico-grids to come into use soon. This research recognises and addresses this research gap in the monitoring and managing of the DC picogrids. It recommends and applies the bottom-up monitoring and management approach in smaller scale grids and in larger scale grids. It innovatively categorises the loads in the grids into dumb loads that do not have intelligence and communication features and smart loads that have these features. While targeting at these ELV DC pico-grids, this research presents solutions that provide users useful information on load classification, load disaggregation, anomaly warning and early fault detection. It provides local and remote sensing with the alternative use of hardware to lessen the computational burden from the main computer. The inclusion of remote monitoring has opened a window of opportunities for Internet of Things (IoT) implementation. These solutions involve the blending of computational intelligence techniques with enhanced algorithms, such as K-Means algorithm, k-Nearest Neighbours (kNN) classification, Naïve Bayes Classification (NBC) Theorem, Statistical Process Control (SPC) and Long Short-Term Memory Recurrent Neural Network (LSTM RNN). As demonstrated in this research, these solutions produce high accuracy results in load classification and early anomaly detection in both AC and DC pico-grids. In addition to the load side, this research features a short-term PV energy forecasting technique that is easily comprehensible to users. This research contributes to the implementation of the Smart Grid with possible IoT features in DC pico-grids

Data-driven modelling, forecasting and uncertainty analysis of disaggregated demands and wind farm power outputs

Correct analysis of modern power supply systems requires to evaluate much wider ranges of uncertainties introduced by the implementation of new technologies on both supply and demand sides. On the supply side, these uncertainties are due to the increased contributions of renewable generation sources (e.g., wind and PV), whose stochastic output variations are difficult to predict and control, as well as due to the significant changes in system operating conditions, coming from the implementation of various control and balancing actions, increased automation and switching functionalities, and frequent network reconfiguration. On the demand side, these uncertainties are due to the installation of new types of loads, featuring strong spatio-temporal variations of demands (e.g., EV charging), as well as due to the deployment of different demand-side management schemes. Modern power supply systems are also characterised by much higher availability of measurements and recordings, coming from a number of recently deployed advanced monitoring, data acquisition and control systems, and providing valuable information on system operating and loading conditions, state and status of network components and details on various system events, transients and disturbances. Although the processing of large amounts of measured data brings its own challenges (e.g., data quality, performance, and incorporation of domain knowledge), these data open new opportunities for a more accurate and comprehensive evaluation of the overall system performance, which, however, require new data-driven analytical approaches and modelling tools. This PhD research is aimed at developing and evaluating novel and improved data-driven methodologies for modelling renewable generation and demand, in general, and for assessing the corresponding uncertainties and forecasting, in particular. The research and methods developed in this thesis use actual field measurements of several onshore and offshore wind farms, as well as measured active and reactive power demands at several low voltage (LV) individual household levels, up to the demands at medium voltage (MV) substation level. The models are specifically built to be implemented for power system analysis and are actually used by a number of researchers and PhD students in Edinburgh and elsewhere (e.g., collaborations with colleagues from Italy and Croatia), which is discussed and illustrated in the thesis through the selected study cases taken from this joint research efforts. After literature review and discussion of basic concepts and definitions, the first part of the thesis presents data-driven analysis, modelling, uncertainty evaluation and forecasting of (predominantly residential) demands and load profiles at LV and MV levels. The analysis includes both aggregation and disaggregation of measured demands, where the latter is considered in the context of identifying demand-manageable loads (e.g., heating). For that purpose, periodical changes in demands, e.g., half-daily, daily, weekly, seasonal and annual, are represented with Fourier/frequency components and correlated with the corresponding exploratory meteorological variables (e.g., temperature, solar irradiance), allowing to select the combination of components maximising the positive or negative correlations as an additional predictor variable. Convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) are then used to represent dependencies among multiple dimensions and to output the estimated disaggregated time series of specific load types (with Bayesian optimisation applied to select appropriate CNN-BiLSTM hyperparameters). In terms of load forecasting, both tree-based and neural network-based models are analysed and compared for the day-ahead and week-ahead forecasting of demands at MV substation level, which are also correlated with meteorological data. Importantly, the presented load forecasting methodologies allow, for the first time, to forecast both total/aggregate demands and corresponding disaggregated demands of specific load types. In terms of the supply side analysis, the thesis presents data-driven evaluation, modelling, uncertainty evaluation and forecasting of wind-based electricity generation systems. The available measurements from both the individual wind turbines (WTs) and the whole wind farms (WFs) are used to formulate simple yet accurate operational models of WTs and WFs. First, available measurements are preprocessed, to remove outliers, as otherwise obtained WT/WF models may be biased, or even inaccurate. A novel simulation-based approach that builds on a procedure recommended in a standard is presented for processing all outliers due to applied averaging window (typically 10 minutes) and WT hysteresis effects (around the cut-in and cut-out wind speeds). Afterwards, the importance of distinguishing between WT-level and WF-level analysis is discussed and a new six-parameter power curve model is introduced for accurate modelling of both cut-in and cut-out regions and for taking into account operating regimes of a WF (WTs in normal/curtailed operation, or outage/fault). The modelling framework in the thesis starts with deterministic models (e.g., CNN-BiLSTM and power curve models) and is then extended to include probabilistic models, building on the Bayesian inference and Copula theory. In that context, the thesis presents a set of innovative data-driven WT and WF probabilistic models, which can accurately model cross-correlations between the WT/WF power output (Pout), wind speed (WS), air density (AD) and wind direction (WD). Vine Copula and Gaussian mixture Copula model (GMCM) are combined, for the first time, to evaluate the uncertainty of Pout values, conditioning on other explanatory variables (which may be either deterministic, or also uncertain). In terms of probabilistic wind energy forecasting, Bayesian CNN-BiLSTM model is used to analyse and efficiently handle high dimensionality of both input meteorological variables (WS, AD and WD) and additional uncertainties due to WF operating regimes. The presented results demonstrate that the developed Vine-GMCM and operational WF model can accurately integrate and effectively correlate all propagated uncertainties, ultimately resulting in much higher confidence levels of the forecasted WF power outputs than in the existing literature

Advanced Methods of Power Load Forecasting

This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal