User-Centered Visual Analytics Approach for Interactive and Explainable Energy Demand Analysis in Prosumer Scenarios

Grimaldo, Ana I.; Novak, Jasminko

doi:10.1007/978-3-030-34995-0_64

Cited by 11 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Small municipal utilities face particular challenges due to their lack of access to advanced modelling and forecasting tools. In this context, [18] proposed a usercentered, visual analytics approach for developing a tool that facilitates interactive and explainable day-ahead forecasting and analysis in prosumer environments. This research included the use of behavioral analysis to examine the connection between consumption patterns and prosumer interaction with energy tools.…”

Section: Literature Reviewmentioning

confidence: 99%

A Recommendation System for Prosumers Based on Large Language Models

Oprea,

Bâra

2024

Preprint

View full text Add to dashboard Cite

As modern technologies, particularly home assistant devices, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information in 15-minute or even 5-minute intervals, including weather forecasts, outputs from Renewable Energy Sources (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the Local Energy Market (LEM). The goal for these prosumers is to reduce costs while ensuring their home’s comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by Large Language Models (LLM), Scikit-llm and Zero-Shot Classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Recommendation System for Prosumers Based on Large Language Models

Oprea,

Bâra

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Innovation in decentralisation, digitalisation and electrification are crucial components of the energy transition and need to be better accounted for in LTES. For example, amplifying auto-consumption from rooftop solar PV systems through the use of residential battery storage and electric vehicles (EVs) was not prominently considered by model designers 20-30 years ago [97][98][99]. Hydrogen, a key energy carrier to decarbonise energy-intensive industries, and how it may co-evolve with renewable electricity infrastructure, continues to be highly unaccounted-for in current techno-economic modelling [100][101][102].…”

Section: Accounting For Innovation In the Energy Sectormentioning

confidence: 99%

Best Practice in Government Use and Development of Long-Term Energy Transition Scenarios

et al. 2022

View full text Add to dashboard Cite

Long-term energy scenarios (LTES) have been serving as an important planning tool by a wide range of institutions. This article focuses on how LTES have been used (and also devised in some cases) in the government sector, and specifically how the new challenges and opportunities brought by the aspiration for the clean energy transition change the way that governments use LTES. The information tends to remain tacit, and a gap exists in understanding the way to enhance LTES use and development at the government level. To address this gap, we draw on the experience from national institutions that are leading the improvement in official energy scenario planning to articulate a set of overarching best practices to (i) strengthen LTES development, (ii) effectively use LTES for strategic energy planning and (iii) enhance institutional capacity for LTES-based energy planning, all in the context of new challenges associated with the clean energy transition. We present implementation experience collected through the International Renewable Agency’s LTES Network activities to exemplify these best practices. We highlight that in the context of the broad and complex challenges of a clean energy transition driven by ambitious climate targets, the LTES-based energy planning methodologies need to evolve, reflecting the changing landscapes, and that more effective and extensive use of LTES in government needs to be further encouraged.

show abstract

“…By contrast, in the field of energy saving and recommendations for energy-related behavior, there is limited literature that elaborates on the rules of producing a particular recommendation. Authors in [156] propose a user-centric and visual analytics approach for developing an interactive and explainable forecasting and analysis of electric power demand in prosumer settings. Moreover, it has been advocated that this would be endorsed by behavioral analysis to enable the treatment of possible relationships between energy usage footprints and the interaction of prosumers with energy analysis tools, including customer portals and recommendation systems.…”

Section: Explainable Ai and Recommender Systemsmentioning

confidence: 99%

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Himeur¹,

Alsalemi²,

Al‐Kababji³

et al. 2021

Preprint

View full text Add to dashboard Cite

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.

show abstract

User-Centered Visual Analytics Approach for Interactive and Explainable Energy Demand Analysis in Prosumer Scenarios

Cited by 11 publications

References 25 publications

A Recommendation System for Prosumers Based on Large Language Models

A Recommendation System for Prosumers Based on Large Language Models

Best Practice in Government Use and Development of Long-Term Energy Transition Scenarios

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Contact Info

Product

Resources

About