Fine-Granular Sensing of Power Consumption - A New Sensing Modality for an Accurate Detection, Prediction and Forecasting of Higher-Level Contextual Information in Smart Environments

Investigating and extracting users' context has always been a main drive for research in the field of smart environments. Context is defined as the set of conditions that characterize a certain situation in which the user exists. An example of user's context is the activity currently performed by the user along with her/his current indoor location. Identifying users' context paves the road for the realization of a wide variety of context-aware services that improve the quality of life for individuals as well as societies. To extract users' context, researchers have resorted to the approach of deploying a huge number of sensors in the environment where users' context has to be extracted. Examples of these sensors are cameras, microphones, motion and contact sensors, state-change sensors and other types of sensors that monitor every aspect of users' context. However, this approach has always been criticized as causing a huge deployment and maintenance overhead for researchers. Moreover, it has been perceived as an intrusive approach by users because they feel themselves surrounded by all possible types of visible sensors. Recent years have seen an increasing adoption of smart metering technologies along with the manufacturing of new appliance-level power sensors that are able to measure the fine-granular power consumption of individual devices in smart environments. As a result, fine-granular sensing of power consumption has emerged as a new sensing modality that avoids the afore-mentioned problems of other sensing modalities. One of the main goals of this thesis is to develop intelligent models that infer and predict several challenging and essential aspects of users' context only based on fine-granular sensing of power consumption. Activities of daily living (ADL) represent an essential part of users' context that has always motivated researchers in the field of smart environments. Context-aware services such as energy conservation in smart environments and ambient assisted living can be realized based on the recognition of users' current activity. In this thesis, we develop SMARTENERGY.KOM, an intelligent hardware/software platform for recognizing activities of users in single-user environments. We build an activity recognition model and evaluate its performance based on a dataset we collect by deploying SMARTENERGY.KOM in two single-user apartments. As an essential part of this thesis, we conduct an in-depth analytical study on the dataset collected by SMARTENERGY.KOM with three main contributions, namely modeling of user's daily behavior, indoor localization based on fine-granular power consumption data, and profiling of user's hourly power consumption. As users tend to follow a daily routine in performing their activities, identifying behavioral patterns of users helps to improve the predictive performance of activity recognition models. We develop an approach that identifies such patterns of user's behavior. Evaluation results show that feeding these patterns into the model of activity recognition leads to a significant improvement in its predictive performance. Indoor location represents another important aspect of users' context that has its potential benefits in realizing location-aware services. We develop a localization model that is able to determine the indoor location of users based on their fine-granular power consumption. Building a profile that characterizes average hourly power consumption of users has its potential benefits in increasing their awareness of the power they consume as well as in detecting abnormal consumption patterns. Driven by this motivation, we develop an approach that identifies and builds this profile based on SMARTENERGY.KOM dataset. As more than one user tend to live, work and reside in one common place, activity recognition models need to cope with the fact that parallel and overlapping activities of several users have to be recognized and assigned to their respective users. This fact has always represented a great challenge for researchers in the field of activity recognition. In this thesis, we address this challenge by developing MLSMARTENERGY.KOM, our platform for activity recognition in multi-user environments. We develop our model for recognizing activities based on the concept of multi-label classification, which exploits label dependency and temporal relations between activities. Forecasting fine-granular power consumption of individual consumers represents another aspect of users' context that has its potential benefits for consumers as well as electric utilities. In this thesis, we develop state-of-the-art forecasting models that are able to forecast hourly, daily, and monthly power consumption of individual buildings based on building characteristics, demographic features of residents, available appliances as well as historical power consumption values. We evaluate these models using a dataset collected by Commission for Energy Regulation (CER) in Ireland

Fine-Granular Sensing of Power Consumption - A New Sensing Modality for an Accurate Detection, Prediction and Forecasting of Higher-Level Contextual Information in Smart Environments

Investigating and extracting users' context has always been a main drive for research in the field of smart environments. Context is defined as the set of conditions that characterize a certain situation in which the user exists. An example of user's context is the activity currently performed by the user along with her/his current indoor location. Identifying users' context paves the road for the realization of a wide variety of context-aware services that improve the quality of life for individuals as well as societies. To extract users' context, researchers have resorted to the approach of deploying a huge number of sensors in the environment where users' context has to be extracted. Examples of these sensors are cameras, microphones, motion and contact sensors, state-change sensors and other types of sensors that monitor every aspect of users' context. However, this approach has always been criticized as causing a huge deployment and maintenance overhead for researchers. Moreover, it has been perceived as an intrusive approach by users because they feel themselves surrounded by all possible types of visible sensors. Recent years have seen an increasing adoption of smart metering technologies along with the manufacturing of new appliance-level power sensors that are able to measure the fine-granular power consumption of individual devices in smart environments. As a result, fine-granular sensing of power consumption has emerged as a new sensing modality that avoids the afore-mentioned problems of other sensing modalities. One of the main goals of this thesis is to develop intelligent models that infer and predict several challenging and essential aspects of users' context only based on fine-granular sensing of power consumption. Activities of daily living (ADL) represent an essential part of users' context that has always motivated researchers in the field of smart environments. Context-aware services such as energy conservation in smart environments and ambient assisted living can be realized based on the recognition of users' current activity. In this thesis, we develop SMARTENERGY.KOM, an intelligent hardware/software platform for recognizing activities of users in single-user environments. We build an activity recognition model and evaluate its performance based on a dataset we collect by deploying SMARTENERGY.KOM in two single-user apartments. As an essential part of this thesis, we conduct an in-depth analytical study on the dataset collected by SMARTENERGY.KOM with three main contributions, namely modeling of user's daily behavior, indoor localization based on fine-granular power consumption data, and profiling of user's hourly power consumption. As users tend to follow a daily routine in performing their activities, identifying behavioral patterns of users helps to improve the predictive performance of activity recognition models. We develop an approach that identifies such patterns of user's behavior. Evaluation results show that feeding these patterns into the model of activity recognition leads to a significant improvement in its predictive performance. Indoor location represents another important aspect of users' context that has its potential benefits in realizing location-aware services. We develop a localization model that is able to determine the indoor location of users based on their fine-granular power consumption. Building a profile that characterizes average hourly power consumption of users has its potential benefits in increasing their awareness of the power they consume as well as in detecting abnormal consumption patterns. Driven by this motivation, we develop an approach that identifies and builds this profile based on SMARTENERGY.KOM dataset. As more than one user tend to live, work and reside in one common place, activity recognition models need to cope with the fact that parallel and overlapping activities of several users have to be recognized and assigned to their respective users. This fact has always represented a great challenge for researchers in the field of activity recognition. In this thesis, we address this challenge by developing MLSMARTENERGY.KOM, our platform for activity recognition in multi-user environments. We develop our model for recognizing activities based on the concept of multi-label classification, which exploits label dependency and temporal relations between activities. Forecasting fine-granular power consumption of individual consumers represents another aspect of users' context that has its potential benefits for consumers as well as electric utilities. In this thesis, we develop state-of-the-art forecasting models that are able to forecast hourly, daily, and monthly power consumption of individual buildings based on building characteristics, demographic features of residents, available appliances as well as historical power consumption values. We evaluate these models using a dataset collected by Commission for Energy Regulation (CER) in Ireland