Framework Convention on Climate Change

Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

Energy models for demand forecasting—A review

Energy use in cities has attracted significant research in recent years. However such a broad topic inevitably results in number of alternative interpretations of the problem domain and the modelling tools used in its study. This paper seeks to pull together these strands by proposing a theoretical definition of an urban energy system model and then evaluating the state of current practice. Drawing on a review of 219 papers, five key areas of practice were identified – technology design, building design, urban climate, systems design, and policy assessment – each with distinct and incomplete interpretations of the problem domain. We also highlight a sixth field, land use and transportation modelling, which has direct relevance to the use of energy in cities but has been somewhat overlooked by the literature to date. Despite their diversity, these approaches to urban energy system modelling share four common challenges in understanding model complexity, data quality and uncertainty, model integration, and policy relevance. We then examine the opportunities for improving current practice in urban energy systems modelling, focusing on the potential of sensitivity analysis and cloud computing, data collection and integration techniques, and the use of activity-based modelling as an integrating framework. The results indicate that there is significant potential for urban energy systems modelling to move beyond single disciplinary approaches towards a sophisticated integrated perspective that more fully captures the theoretical intricacy of urban energy systems.

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A review of urban energy system models: Approaches, challenges and opportunities

Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review

We present a comprehensive review of modelling approaches and associated software tools that address district-level energy systems. Buildings play an important role in urban energy systems regarding both the demand and supply of energy. It is no longer sufficient to simulate building energy use assuming isolation from the microclimate and energy system in which they operate, or to model an urban energy system without consideration of the buildings that it serves. This review complements previous studies by focussing on models that address district-level interactions in energy systems, and by assessing the capabilities of the software tools available alongside the theory of the modelling approaches used. New models and tools that address these district-level interactions are reviewed and their competences assessed. These are divided into the following sections: district energy systems (including heat networks, multi-energy systems and low-temperature networks), renewable energy generation (including solar, bioenergy, wind and the related topic of seasonal storage), and the urban microclimate as it relates to energy demands. The scope and detail covered by twenty cross-disciplinary tools is summarised in a matrix; many other tools that focus on specific areas are also discussed. We end by summarising the current state of district-scale urban energy modelling as it relates to the built environment, along with our perspective on future challenges and research directions.

Weisheng Zhou

Papers

Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects

Optimal operation of a grid-connected hybrid PV/fuel cell/battery energy system for residential applications

Multi-criteria evaluation for the optimal adoption of distributed residential energy systems in Japan

Energy–exergy analysis and optimization of the solar-boosted Kalina cycle system 11 (KCS-11)

Optimal option of distributed energy systems for building complexes in different climate zones in China