B4B is a multi-year, multi-stakeholder project focused on developing methods to harness big data from smart meters, building management systems and the Internet of Things devices, to reduce energy consumption, increase comfort, respond flexibly to user behaviour and local energy supply and demand, and save on installation maintenance costs. This will be done through the development of faster and more efficient Machine Learning and Artificial Intelligence models and algorithms. The project is geared to existing utility buildings such as commercial and institutional buildings.
DOCUMENT
In this research, the experiences and behaviors of end-users in a smart grid project are explored. In PowerMatching City, the leading Dutch smart grid project, 40 households were equipped with various decentralized energy sources (PV and microCHP), hybrid heat pumps, smart appliances, smart meters and an in-home display. Stabilization and optimization of the network was realized by trading energy on the market. To reduce peak loads on the smart grid, several types of demand side management were tested. Households received feedback on their energy use either based on costs, or on the percentage of consumed energy that had been produced locally. Furthermore, devices could be controlled automatically, smartly or manually to optimize the energy use of the households. Results from quantitative and qualitative research showed that: (1) feedback on costs reduction is valued most; (2) end-users preferred to consume self-produced energy (this may even be the case when, from a cost or sustainability perspective, it is not the most efficient strategy to follow); (3) automatic and smart control are most popular, but manually controlling appliances is more rewarding; (4) experiences and behaviors of end-users depended on trust between community members, and on trust in both technology (ICT infrastructure and connected appliances) and the participating parties.
DOCUMENT
Machine learning models have proven to be reliable methods in classification tasks. However, little research has been done on classifying dwelling characteristics based on smart meter & weather data before. Gaining insights into dwelling characteristics can be helpful to create/improve the policies for creating new dwellings at NZEB standard. This paper compares the different machine learning algorithms and the methods used to correctly implement the models. These methods include the data pre-processing, model validation and evaluation. Smart meter data was provided by Groene Mient, which was used to train several machine learning algorithms. The models that were generated by the algorithms were compared on their performance. The results showed that Recurrent Neural Network (RNN) 2performed the best with 96% of accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrices were used to produce classification reports, which can indicate which of the models work the best for this specific problem. The models were programmed in Python.
DOCUMENT
This study explores how households interact with smart systems for energy usage, providing insights into the field's trends, themes and evolution through a bibliometric analysis of 547 relevant literature from 2015 to 2025. Our findings discover: (1) Research activity has grown over the past decade, with leading journals recognizing several productive authors. Increased collaboration and interdisciplinary work are expected to expand; (2) Key research hotspots, identified through keyword co-occurrence, with two (exploration and development) stages, highlighting the interplay between technological, economic, environmental, and behavioral factors within the field; (3) Future research should place greater emphasis on understanding how emerging technologies interact with human, with a deeper understanding of users. Beyond the individual perspective, social dimensions also demand investigation. Finally, research should also aim to support policy development. To conclude, this study contributes to a broader perspective of this topic and highlights directions for future research development.
MULTIFILE
Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
DOCUMENT
In PowerMatching City, the leading Dutch smart grid project, 40 households participated in a field laboratory designed for sustainable living. The participating households were equipped with various decentralized energy sources (PV and micro combined heat-power units), hybrid heat pumps, smart appliances, smart meters, and an in-home display. Stabilization and optimization of the network was realized by trading energy on the market. To reduce peak loads on the smart grid and to be able to make optimal use of the decentralized energy sources, two energy services were developed jointly with the end users: Smart Cost Savings enabled users to keep the costs of energy consumption as low as possible, and Sustainable Together enabled them to become a sustainable community. Furthermore, devices could be controlled automatically, smartly, or manually to optimize the energy use of the households. Quantitative and qualitative studies were conducted to provide insight into the experiences and behaviours of end users. In this chapter, these experiences and behaviours are described. The chapter argues that end users: (1) prefer to consume self-produced energy, even when it is not the most efficient strategy to follow, (2) prefer feedback on costs over feedback on sustainability, and (3) prefer automatic and smart control, even though manual control of appliances felt most rewarding. Furthermore, we found that experiences and behaviours were fully dependent on trust between community members, and on trust in both technology (ICT infrastructure and connected appliances) and the participating parties.
LINK
The objective of this book ‘An introduction to Smart Dairy Farming’ is to provide insight in the development of the Smart Dairy Farming (SDF) concept and advise as to how to apply this knowledge in the field of activities of students from universities of applied science. The information in this book includes background information and comprehensive insight in the concept of SDF.
DOCUMENT
In discussions on smart grids, it is often stated that residential end-users will play a more active role in the management of the electric power system. Experience in practice on how to empower end-users for such a role is however limited. This paper presents a field study in the first phase of the PowerMatching City project in which twenty-two households were equipped with demand-response-enabled heating systems and white goods. Although end-users were satisfied with the degree of living comfort afforded by the smart energy system, the user interface did not provide sufficient control and energy feedback to support an active contribution to the balancing of supply and demand. The full potential of demand response was thus not realized. The second phase of the project builds on these findings by design, implementation and evaluation of an improved user interface in combination with two demand response propositions. © 2013 IEEE.
DOCUMENT
mHealth 24/7 is een dienst die diabetespatiënten helpt om op eenvoudige wijze toezicht te houden op hun eigen gezondheid. mDiabetes 24/7 is een prototype app binnen de dienst mHealth 24/7. Op dit moment kunnen patiënten met het prototype van de app hun bloedsuikerwaardes, een eetdagboek en de hoeveelheid toegediende insuline bijhouden. mHealth 24/7 heeft de wens geformuleerd om haar informatievoorziening aan diabetespatiënten verder uit te breiden, door gepersonaliseerd inzicht te geven in de oorzaak van stijgingen en dalingen van hun bloedsuikerwaarden. Meer informatie stelt de patiënt in staat om beter gemotiveerde maatregelen te nemen en stimuleert therapietrouw waarmee later complicaties kunnen worden voorkomen. Dit verbetert de kwaliteit van leven en vermindert kosten.In het project is gerealiseerd dat data uit een activity tracker en omgevingstemperatuur ingelezen wordt in de app en wordt geïntegreerd met bestaande data zoals bloedsuikerwaarde. Daarnaast kunnen patiënten handmatig aangeven hoe ze zich voelen. Patiënten krijgen daarmee inzicht in het effect van activiteit, omgevingstemperatuur en stemming op fluctuaties in bloedsuikerwaardes. In een pilot met 25 proefpersonen is de technische werking van de verrijkte app getest evenals de functionaliteit.Er is aangetoond dat de app werkt en dat voor gebruikers de verrijking van de informatie in de app met hartslag, omgevingstemperatuur en stemming van toegevoegde waarde is. Wel blijkt dat een app zoals deze foutloos en realtime moet werken en de gebruiksinterface dusdanig moet werken, dat de gebruikers er uitsluitend gemak van ondervinden. Diabetes is een arbeidsintensieve ziekte en nog meer werk is ongewenst!Als in een volgende pilot meer data kan worden verzameld, kan worden gewerkt aan het voorspellen van fluctuaties in bloedsuikerwaardes waardoor een patiënt ook voortijdig gewaarschuwd kan worden.Vanuit verschillende marktpartijen zoals ziekenhuizen en zorgverzekeraars is interesse getoond voor het project. Gezamenlijk gaan deze partijen aanspraak doen op tijdelijke financiering vanuit de “Beleidsregel Innovatie Kleinschalige Experimenten.
DOCUMENT
In this paper we focus on the inclusion of decentralized energy production in the current energy infrastructure, and the changing role of the consumer towards a producer and an active participant in the energy value network.
DOCUMENT
