Energy Planning in the Urban Context: Challenges and Perspectives

Cajot, Sébastien; Peter, Markus; Bahu, Jean-Marie; Koch, Andreas; Maréchal, François

doi:10.1016/j.egypro.2015.11.752

Cited by 47 publications

(27 citation statements)

References 17 publications

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“…In the end, they are a result of the degree of urban complexity coinciding with institutional complexity at the community level. The complexity of energy target implementation has been characterized several times as 'wicked' (Cajot et al, 2015). The reference to Rittel's & Webbers' concept of 'wicked problems' from 1973 il-lustrates that implementing energy targets at the community level is not just another governance challenge, but requires strategic, systematic, and continuous actions.…”

Section: Problem Statementmentioning

confidence: 99%

“…National legislation, natural preconditions, physical restrictions, citizens' interests or their economic situation were named besides others as main challenges. These aspects are spread over all four domains of community and exemplify that the inclusion of various actors, levels, time-horizons and disciplines is necessary when producing and implementing energy strategies (Cajot et al, 2015).…”

Section: Complexity Of Energy Planning On Community Levelmentioning

confidence: 99%

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The application of municipal renewable energy policies at community level in Denmark: A taxonomy of implementation challenges

Petersen

2018

Sustainable Cities and Society

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Section: Problem Statementmentioning

confidence: 99%

Section: Complexity Of Energy Planning On Community Levelmentioning

confidence: 99%

The application of municipal renewable energy policies at community level in Denmark: A taxonomy of implementation challenges

Petersen

2018

Sustainable Cities and Society

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“…India's urban population is expected to grow from 31.6 per cent to 57.7 per cent by 2050, with additional impacts on energy consumption and carbon emissions [2]. The task of managing and reducing energy demand, energy consumption and energy-related carbon emissions is often a challenge for urban planners [3,4].…”

Section: Introductionmentioning

confidence: 99%

Smart Energy Management Policy in India—A Review

et al. 2019

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India accounts for six per cent of the world’s primary energy consumption. Rapid urbanization and rapid urban population growth have had a serious impact on energy consumption and subsequent carbon emissions. In particular, cities face a complex and interrelated set of challenges across different sectors (building environment, mobility, water and waste management and public services). Re-examining these challenges by integrating smart energy management (SEM) principles is critical for sustainable and low-carbon urban development. In addition, managing energy footprint is one of the most challenging goals for cities, and as existing cities evolve and transform into smart cities, SEM becomes an integral part of the urban transformation. This article comprehensively reviews the different SEM technologies for different sectors (construction, transportation, public services, water and waste), the policies, and the current challenges and opportunities for SEM policy governance in India. Making urban energy smart can manage a city’s energy footprint and have a positive impact on future carbon emissions.

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“…New urban and building energy models are currently under development, however their application to real areas is restrained by the limited amount of detailed openly available energy demand data [5,6,7,8]. While residential energy demand data are available to some extent, service sector and industrial data are often not (openly) available at all.…”

Section: Introductionmentioning

confidence: 99%

Statistical Data-Driven Regression Method for Urban Electricity Demand Modelling

Voulis

Warnier

Brazier

2018

2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Syst

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As the focus of the energy transition within cities worldwide moves towards local communities and neighbourhoods, the need for insights in the dynamics of local electricity demand increases. Detailed local electricity demand information is, however, often not available. This paper proposes a statistical data-driven method to model local electricity demand for mixed urban areas, using a combination of other openly available datasets. Such datasets however are mutually incompatible without further conversion. The proposed method overcomes this problem. Linear regression is used to combine these different datasets, whereby the regression coefficients have the meaning of scaling factors for different types of electricity consumers (households, offices, shops, etc.). The method is calibrated and validated using respectively a training and a test dataset of Dutch municipalities, yielding R-squared values for most consumer types between 61% and 98%. The application of the method for local electricity demand modelling is illustrated for three Dutch municipalities with different consumer compositions.

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Energy Planning in the Urban Context: Challenges and Perspectives

Cited by 47 publications

References 17 publications

The application of municipal renewable energy policies at community level in Denmark: A taxonomy of implementation challenges

The application of municipal renewable energy policies at community level in Denmark: A taxonomy of implementation challenges

Smart Energy Management Policy in India—A Review

Statistical Data-Driven Regression Method for Urban Electricity Demand Modelling

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