Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.Peer reviewedFinal Published versio

Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method which incorporates appliance usage patterns (AUPs) to improve performance of active load identi- fication and forecasting. In the first stage, the AUPs of a given residence were learnt using a spectral decomposition based standard NILM algorithm. Then, learnt AUPs were utilized to bias the priori probabilities of the appliances through a specifically constructed fuzzy system. The AUPs contain likelihood measures for each appliance to be active at the present instant based on the recent activity/inactivity of appliances and the time of day. Hence, the priori probabilities determined through the AUPs increase the active load identification accuracy of the NILM algorithm. The proposed method was successfully tested for two standard databases containing real household measurements in USA and Germany. The proposed method demonstrates an improvement in active load estimation when applied to the aforementioned databases as the proposed method augments the smart meter readings with the behavioral trends obtained from AUPs. Furthermore, a residential power consumption forecasting mechanism, which can predict the total active power demand of an aggregated set of houses, five minutes ahead of real time, was successfully formulated and implemented utilizing the proposed AUP based technique

Machine learning techniques for sensor-based household activity recognition and forecasting

Thanks to the recent development of cheap and unobtrusive smart-home sensors, ambient assisted living tools promise to offer innovative solutions to support the users in carrying out their everyday activities in a smoother and more sustainable way. To be effective, these solutions need to constantly monitor and forecast the activities of daily living carried out by the inhabitants. The Machine Learning field has seen significant advancements in the development of new techniques, especially regarding deep learning algorithms. Such techniques can be successfully applied to household activity signal data to benefit the user in several applications. This thesis therefore aims to produce a contribution that artificial intelligence can make in the field of activity recognition and energy consumption. The effective recognition of common actions or the use of high-consumption appliances would lead to user profiling, thus enabling the optimisation of energy consumption in favour of the user himself or the energy community in general. Avoiding wasting electricity and optimising its consumption is one of the main objectives of the community. This work is therefore intended as a forerunner for future studies that will allow, through the results in this thesis, the creation of increasingly intelligent systems capable of making the best use of the user's resources for everyday life actions. Namely, this thesis focuses on signals from sensors installed in a house: data from position sensors, door sensors, smartphones or smart meters, and investigates the use of advanced machine learning algorithms to recognize and forecast inhabitant activities, including the use of appliances and the power consumption. The thesis is structured into four main chapters, each of which represents a contribution regarding Machine Learning or Deep Learning techniques for addressing challenges related to the aforementioned data from different sources. The first contribution highlights the importance of exploiting dimensionality reduction techniques that can simplify a Machine Learning model and increase its efficiency by identifying and retaining only the most informative and predictive features for activity recognition. In more detail, it is presented an extensive experimental study involving several feature selection algorithms and multiple Human Activity Recognition benchmarks containing mobile sensor data. In the second contribution, we propose a machine learning approach to forecast future energy consumption considering not only past consumption data, but also context data such as inhabitants’ actions and activities, use of household appliances, interaction with furniture and doors, and environmental data. We performed an experimental evaluation with real-world data acquired in an instrumented environment from a large user group. Finally, the last two contributions address the Non-Intrusive-Load-Monitoring problem. In one case, the aim is to identify the operating state (on/off) and the precise energy consumption of individual electrical loads, considering only the aggregate consumption of these loads as input. We use a Deep Learning method to disaggregate the low-frequency energy signal generated directly by the new generation smart meters being deployed in Italy, without the need for additional specific hardware. In the other case, driven by the need to build intelligent non-intrusive algorithms for disaggregating electrical signals, the work aims to recognize which appliance is activated by analyzing energy measurements and classifying appliances through Machine Learning techniques. Namely, we present a new way of approaching the problem by unifying Single Label (single active appliance recognition) and Multi Label (multiple active appliance recognition) learning paradigms. This combined approach, supplemented with an event detector, which suggests the instants of activation, would allow the development of an end-to-end NILM approach

Non-intrusive load monitoring based on low frequency active power measurements

A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on ac- tive power signal is presented. This method works e ectively with a single active power measurement taken at a low sampling rate (1 s). The proposed method utilizes the Karhunen Lo ́ eve (KL) expan- sion to decompose windows of active power signals into subspace components in order to construct a unique set of features, referred to as signatures, from individual and aggregated active power signals. Similar signal windows were clustered in to one group prior to feature extraction. The clustering was performed using a modified mean shift algorithm. After the feature extraction, energy levels of signal windows and power levels of subspace components were utilized to reduce the number of possible ap- pliance combinations and their energy level combinations. Then, the turned on appliance combination and the energy contribution from individual appliances were determined through the Maximum a Pos- teriori (MAP) estimation. Finally, the proposed method was modified to adaptively accommodate the usage patterns of appliances at each residence. The proposed NILM method was validated using data from two public databases: tracebase and reference energy disaggregation data set (REDD). The pre- sented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned on appliance combinations in real households. Furthermore, the results emphasise the importance of clustering and the integration of the usage behaviour pattern in the proposed NILM method for real household