In 2020, residential sector loads reached 25% of the overall electrical consumption in Europe and it is foreseen to stabilise at 29% by 2050. However, this relatively small increase demands, among ...others, changes in the energy consuming behaviour of households. To achieve this, Demand Response (DR) has been identified as a promising tool for unlocking the hidden flexibility potential of residential consumption. In this work, a holistic incentive-based DR framework aiming towards load shifting is proposed for residential applications. The proposed framework is characterised by several innovative features, mainly the formulation of the optimisation problem, which models user satisfaction and the economic operation of a distributed household portfolio, the customised load forecasting algorithm, which employs an adjusted Gradient Boosting Tree methodology with enhanced feature extraction and, finally, a disaggregation tool, which considers electrical features and time of use information. The DR framework is first validated through simulation to assess the business potential and is then deployed experimentally in real houses in Northern Greece. Results demonstrate that a mean 1.48% relative profit can be achieved via only load shifting of a maximum of three residential appliances, while the experimental application proves the effectiveness of the proposed algorithms in successfully managing the load curves of real houses with several residents. Correlations between market prices and the success of incentive-based load shifting DR programs show how wholesale pricing should be adjusted to ensure the viability of such DR schemes.
As microgrids have gained increasing attention over the last decade, more and more applications have emerged, ranging from islanded remote infrastructures to active building blocks of smart grids. To ...optimally manage the various microgrid assets towards maximum profit, while taking into account reliability and stability, it is essential to properly schedule the overall operation. To that end, this paper presents an optimal scheduling framework for microgrids both for day-ahead and real-time operation. In terms of real-time, this framework evaluates the real-time operation and, based on deviations, it re-optimises the schedule dynamically in order to continuously provide the best possible solution in terms of economic benefit and energy management. To assess the solution, the designed framework has been deployed to a real-life microgrid establishment consisting of residential loads, a PV array and a storage unit. Results demonstrate not only the benefits of the day-ahead optimal scheduling, but also the importance of dynamic re-optimisation when deviations occur between forecasted and real-time values. Given the intermittency of PV generation as well as the stochastic nature of consumption, real-time adaptation leads to significantly improved results.
•Occupancy prediction based on historical data and current context.•Development of 2 different algorithmic approaches based on Markov models.•Experimentation based on real-life accurate data for 3 ...different types of spaces.•Sufficient results giving a significant improvement compared to existing approaches.
In this paper a building occupancy prediction method is presented, which is based on the spatio-temporal analysis of historical data (occupancy modelling) and further relies heavily on current contextual information, being therefore suitable for providing real-time prediction. Two different algorithmic approaches are proposed, based on Markov models, revealing how context awareness adds the capability of rapidly adjusting to current conditions and capturing unexpected events, as opposed to capturing only typical occupancy fluctuation expected on a regular basis. Both proposed approaches are evaluated against accurate real-life data collected from a tertiary building, achieving notable results which outperform currently used methods.
Automated real-time occupancy monitoring in buildings plays an important role in increasing energy efficiency and provides facility managers with useful information about the usage of different ...spaces. In this paper, a novel approach is proposed for estimating real-time occupancy in buildings, based on Conditional Random Field probabilistic models, utilizing data from different sensor types. Three different types of occupancy information are considered: presence/absence, actual number of occupants and occupancy density. The proposed occupancy estimation method has been applied to four spaces with different characteristics in a real-life testbed environment. Experimental results revealed that the proposed method yielded high accuracy for different sensor combinations in all tested configurations regarding the occupancy granularity and the space type, and outperformed the Hidden Markov Model based method.
Display omitted
•We present a real-time building occupancy monitoring system using sensor fusion.•Occupancy estimation is based on probabilistic Conditional Random Field models.•Different combinations of simple sensors are evaluated in four different spaces.•Low NRMSE values were achieved by the proposed method.•The most efficient sensor combinations based on multiple criteria are presented.
Cities are challenged with increasing population growth and need to implement smart solutions to become more resilient to economic, environmental, and social challenges posed by ongoing urbanization. ...This study reviewed business model development frameworks and developed a practical tool to help cities assess business models by adapting components of the Business Model Canvas (BMC) and adding new ones that operationalize the smart city dimensions. The Smart City BMC (SC-BMC) proposed provides a practical framework that supports developing and communicating a more holistic and integrated view of a smart city business model. It also supports creatively innovating toward more sustainable value creation. As a framework, the SC-BMC bridges sustainable value creation for business model development and smart city innovation.
Smart and sustainable cities are expected to form a cornerstone for achieving resource efficiency and sustainability worldwide. In this specific study we introduce a holistic framework for ...determining a repository of key performance indicators (KPIs) that are able to evaluate both business-as-usual and novel technologies and services related to smart city solutions. The framework includes six steps: (a) Clustering of the technology/service solutions into groups called Transition Tracks; (b) definition of the main groups of stakeholders; (c) definition of KPIs dimensions (or domains); (d) definition of KPIs repository per dimension; (e) definition of the scope of evaluation per KPI; and (f) threshold definition per KPI. The implementation of the proposed framework led to the development of a repository of 75 KPIs categorized in six dimensions (technical, environmental, economic, social, ICT and legal KPIs) with the corresponding levels of assessment and stakeholders’ group of interest. The proposed repository can serve as a great basis for similar projects to monitor and evaluate the performance of their solutions. Tips and guidance based on the actual implementation and lessons learned from a smart city project are provided.
Summary
In this paper, an intelligent occupancy‐based, automated building control system is initially introduced, which has the capability of reducing energy consumption while respecting the ...occupants' comfort and actions inside building areas. Data stemming from a multisensorial network is combined with implicitly extracted information, since no direct feedback is expected by the users, towards unsupervised monitoring and control of building operation. However, due to significantly higher costs of actuating equipment and the reluctance that some end users show in accepting fully automated solutions, a new hybrid approach is conceptualized and presented; the automated decision support system is supplemented with a persuasion mechanism aimed at increasing energy savings, through raising user awareness. The persuasive methodology employs a continuous feedback mechanism in order to select the optimal incentive strategy by taking into account user success rates, in terms of requested actions, as well as available mechanisms, and daily harassment.
This study introduces an unsupervised occupancy‐driven decision support system for unobtrusive building automation, which offers energy efficiency while preserving end‐users visual and thermal comfort in tertiary buildings. The system was demonstrated and validated in real‐life conditions achieving savings from 18% up to 21.5%. Finally, a methodology for extra margins of long‐term savings was explored through end‐user persuasion to raise awareness and change occupant behavior.
E-health home based solutions reduce healthcare costs and allow aging population to continue their daily life independently. Our objective, is to combine simple IoT sensors and machine learning ...techniques, in order to provide a home based solution that is able to detect behavioral changes of elderly people who live alone. For this purpose, we introduce a non-intrusive, spatio-temporal abnormal behavior detection approach. In this approach, motion and door sensor signals are elaborated to produce contextual metrics, which are filtered from any deviant observations, after performing a silhouette analysis on five outlier detection algorithms. Next, the combination of a classification and a regression based approach is proposed for detecting abnormalities in the metrics, both in the contexts of space and time. IoT sensor data from ten elderly people houses have been collected and seven different machine learning algorithms have been analyzed in order to evaluate the performance of the individual as well as the combined approach.
In this work we analyze the security requirements and challenges of e-Health Internet of Things (IoT) applications and propose a complete architecture to address them. This architecture combines ...VICINITY IoT Framework security features together with General Data Protection Regulation (GDPR) compliant mechanisms in order to provide secure e-Health services to elders and middle-aged people. We also demonstrate how an Ambient Assisted Living (AAL) and an mHealth application were designed and implemented, addressing the current security and privacy requirements.
The integration of distributed battery energy storage systems has started to increase in power systems recently, as they can provide multiple services to the system operator, i.e. frequency ...regulation, system peak shaving, backup power etc. Additionally, batteries can be installed even in facilities where the installation of renewable energy sources are impossible, such as apartments within urban areas. Consequently, an aggregator could deploy distributed battery systems to households under his portfolio, utilising them to capitalize on Demand Response services while sharing benefits of electricity cost reduction with them. To enable that, this paper provides an integrated solution for monitoring, scheduling, and controlling a residential battery energy storage system. The proposed system has been realised in the context of inteGRIDy project to a pilot site that consist of 4 different dwellings that are located in Northern Greece. The analysis of the pilot results revealed that battery systems could exploit the variation in electricity price in order to succeed some profit alongside with the provided energy services.
PDF
