Positive Energy Districts: The 10 Replicated Solutions in Maia, Reykjavik, Kifissia, Kladno and Lviv

Fatima, Zarrin; Padilla, Marielisa; Kuzmic, Michal; Huovila, Aapo; Schaj, Gretel; Effenberger, Niklas

doi:10.3390/smartcities6010001

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…If there is no obstacle delaying these dynamics, entrepreneurs and investors should be willing to invest and scale the PEDs concept. Nevertheless, financing and scaling up are by now important challenges of PEDs (Bossi et al, 2020;Fatima et al, 2023). Therefore, we have performed expert interviews to examine our conceptual model and gain insight into its applicability.…”

Section: Theory Developmentmentioning

confidence: 99%

“…According to recent synthesis of European PED and related concepts (Bossi et al, 2020;Zhang, 2023) a large amount of PEDs are currently still in the development stage, commonly financed with private and public money usually through national and European grants under the framework of pilot and demonstration projects (Fatima et al, 2023). These studies emphasize the lack of appropriate business models and funding schemes as one of the most frequent challenge in the implementation of PEDs; conversely the existence of such schemes is one of the most frequently cited success factors.…”

Section: Introductionmentioning

confidence: 99%

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Network dynamics of positive energy districts: a coevolutionary business ecosystem analysis

Zapata Riveros,

Scacco,

Ulli-Beer

2024

Front. Sustain.

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IntroductionAmid the rising interest in sustainable urban development, Positive Energy Districts (PEDs) have become a focus of research. This study examines the dynamic processes that influence the development and scalability of PEDs from a co-evolutionary business ecosystem perspective.MethodsTo delve into the dynamics of Positive Energy Districts, we applied the business ecosystem framework to a real-world case study, namely the Hunziker Areal. Our research methodology involved the development and validation of a high-level conceptual model. This was achieved through workshops and guided interviews with experts engaged in pilot and research projects related to PEDs.ResultsThe study highlights the significance of employing a systemic approach to evaluate the potential of PEDs in enhancing housing sustainability while creating value for diverse stakeholders. Through the utilization of causal loop diagrams, key feedback loops explaining the diffusion of PEDs are identified. Moreover, the study reveals varying perceptions of PED utility among stakeholders, who assess the impact using different Key Performance Indicators (KPIs) such as CO2 target achievement and well-being. Key factors influencing technology adoption, such as low prosumer electricity unit cost, are also identified.DiscussionOur findings further shed light on crucial aspects affecting value capture and the attractiveness of the ecosystem to investors. Additionally, the study underscores the critical role of supportive policies and regulations in facilitating the diffusion and scalability of Positive Energy Districts.

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Section: Theory Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network dynamics of positive energy districts: a coevolutionary business ecosystem analysis

Zapata Riveros,

Scacco,

Ulli-Beer

2024

Front. Sustain.

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“…The difficulties comprise an unsettled definition of PEDs [5], the current need for multiple development phases [6], insufficient implementation tools [7], and other technical and non-technical challenges [8]. Many of the projects are still in their early phases [9,10]. Lessons learned from a project on one specific site are not easily transferred to another because of the context discrepancy.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts

Han,

Canli,

Shah

et al. 2024

Buildings

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The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments.

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“…Existing urban neighborhoods, particularly in older and culturally protected parts of a city, can be seen as another key barrier for PED mainstreaming [10,11]. Overall, existing studies suggest that PEDs have the potential to balance and support the energy grid through local energy generation, the reduction of energy consumption and greenhouse gas emissions, and the promotion of sustainability in urban areas.…”

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confidence: 99%

ChatGPT for Fast Learning of Positive Energy District (PED): A Trial Testing and Comparison with Expert Discussion Results

2023

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Positive energy districts (PEDs) are urban areas which seek to take an integral approach to climate neutrality by including technological, spatial, regulatory, financial, legal, social, and economic perspectives. It is still a new concept and approach for many stakeholders. ChatGPT, a generative pre-trained transformer, is an advanced artificial intelligence (AI) chatbot based on a complex network structure and trained by the company OpenAI. It has the potential for the fast learning of PED. This paper reports a trial test in which ChatGPT is used to provide written formulations of PEDs within three frameworks: challenge, impact, and communication and dissemination. The results are compared with the formulations derived from over 80 PED experts who took part in a two-day workshop discussing many aspects of PED research and development. The proposed methodology involves querying ChatGPT with specific questions and recording its responses. Subsequently, expert opinions on the same questions are provided to ChatGPT, aiming to elicit a comparison between the two sources of information. This approach enables an evaluation of ChatGPT’s answers in relation to the insights shared by domain experts. By juxtaposing the outputs, a comprehensive assessment can be made regarding the reliability, accuracy, and alignment of ChatGPT’s responses with expert viewpoints. It is found that ChatGPT can be a useful tool for the rapid formulation of basic information about PEDs that could be used for its wider dissemination amongst the general public. The model is also noted as having a number of limitations, such as providing pre-set single answers, a sensitivity to the phrasing of questions, a tendency to repeat non-important (or general) information, and an inability to assess inputs negatively or provide diverse answers to context-based questions. Its answers were not always based on up-to-date information. Other limitations and some of the ethical–social issues related to the use of ChatGPT are also discussed. This study not only validated the possibility of using ChatGPT to rapid study PEDs but also trained ChatGPT by feeding back the experts’ discussion into the tool. It is recommended that ChatGPT can be involved in real-time PED meetings or workshops so that it can be trained both iteratively and dynamically.

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Positive Energy Districts: The 10 Replicated Solutions in Maia, Reykjavik, Kifissia, Kladno and Lviv

Cited by 5 publications

References 33 publications

Network dynamics of positive energy districts: a coevolutionary business ecosystem analysis

Network dynamics of positive energy districts: a coevolutionary business ecosystem analysis

Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts

ChatGPT for Fast Learning of Positive Energy District (PED): A Trial Testing and Comparison with Expert Discussion Results

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