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In this paper we forecast energy demand at the local level using a new two-dimensional downscaling methodology that respects regional and subnational variation and, as well as consistency at the aggregate level. This methodology combines the one-dimensional statistical downscaling and the Iterative Proportional Fitting (IPF) procedure. Previous projection studies based on downscaling methods have focused on allocating one-dimensional quantity indicators such as GDP, population, and emissions, into geographically or administratively lower levels. In case of energy demand projections, however, downscaling involves not only regional allocation of energy demand quantities but also energy mixes of the demands in the regions. Our novel two-dimensional downscaling methodology allows us to downscale national energy projections in both demand quantities and their energy mix to regional levels by combining the statistical downscaling and the IPF procedure. We illustrate the methodology by deriving energy demand projections for provinces and metropolitan cities in Korea from national scenarios. In addition, we provide two more simple applications of the IPF to show utilization potentials of the IPF in energy demand forecasting and statistics.

The development of single-photon counting detectors and arrays has made tremendous steps in recent years, not the least because of various new applications, e.g., LIDAR devices. In this work, a 3D imaging device based on real thermal light intensity interferometry is presented. By using gated SPAD technology, a basic 3D scene is imaged in reasonable measurement time. Compared to conventional approaches, the proposed synchronized photon counting allows the use of more light modes to enhance 3D ranging performance. Advantages like robustness to atmospheric scattering or autonomy by exploiting external light sources can make this ranging approach interesting for future applications.

Integrated assessment models (IAMs) have emerged as key tools for building and assessing long term climate mitigation scenarios. Due to their central role in the recent IPCC assessments, and international climate policy analyses more generally, and the high uncertainties related to future projections, IAMs have been critically assessed by scholars from different fields receiving various critiques ranging from adequacy of their methods to how their results are used and communicated. Although IAMs are conceptually diverse and evolved in very different directions, they tend to be criticised under the umbrella of ‘IAMs’. Here we first briefly summarise the IAM landscape and how models differ from each other. We then proceed to discuss six prominent critiques emerging from the recent literature, reflect and respond to them in the light of IAM diversity and ongoing work and suggest ways forward. The six critiques relate to (a) representation of heterogeneous actors in the models, (b) modelling of technology diffusion and dynamics, (c) representation of capital markets, (d) energy-economy feedbacks, (e) policy scenarios, and (f) interpretation and use of model results.

This study describes the models employed, the main scenario constraints and the energy and climate policy assumptions for a companion study on “European decarbonisation pathways under alternative technological and policy choices: A multi-model analysis”. We describe the main characteristics, the coverage and applications of seven large-scale energy-economy EU models used in the aforementioned study (PRIMES, GEM-E3, TIMES-PanEu, NEMESIS, WorldScan, Green-X and GAINS). The alternative scenarios modelled and the underlying assumptions and constraints are also specified. The main European energy and climate policies assumed to be implemented in the Reference scenario are outlined. We explain the formula used for the decomposition of carbon emissions reduction achieved in the basic decarbonisation scenario relative to the reference. Detailed model results for the power generation mix and RES deployment in the basic decarbonisation scenario in the EU are also presented. We conclude the description of our modelling approach with a brief comparison of the strengths and weaknesses of the models used. Keywords: PRIMES, GEM-E3, TIMES-PanEu, NEMESIS, WorldScan, Green-X, GAINS

A collective intelligent toolbox has been studied and developed to co-operate multi complex related information systems based on a linked model framework. First the linked model framework is defined in a very formal manner. The mathematic abstract specification provides the basis for handling data exchange among various linked models as well as data from those models integrated into a data warehouse. In this context, an ETL (extracting-transforming-loading) process has been defined. Hereafter, the collective intelligent toolbox is developed based on the multidimensional database stored in the data warehouse. To approve of our concepts, a sectoral linked model framework with its collective intelligent toolbox will be presented as a practical case study. Several typical examples of the extracted-transformed-linked and published key data are represented.

The Graph Isomorphism problem restricted to graphs of bounded treewidth or bounded tree distance width are known to be solvable in polynomial time [Bod90],[YBFT99]. We give restricted space algorithms for these problems proving the following results: - Isomorphism for bounded tree distance width graphs is in L and thus complete for the class. We also show that for this kind of graphs a canon can be computed within logspace. - For bounded treewidth graphs, when both input graphs are given together with a tree decomposition, the problem of whether there is an isomorphism which respects the decompositions (i.e. considering only isomorphisms mapping bags in one decomposition blockwise onto bags in the other decomposition) is in L. - For bounded treewidth graphs, when one of the input graphs is given with a tree decomposition the isomorphism problem is in LogCFL. - As a corollary the isomorphism problem for bounded treewidth graphs is in LogCFL. This improves the known TC1 upper bound for the problem given by Grohe and Verbitsky [GroVer06]., STACS conference 2010, 12 pages

The Mitigation Efforts Calculator (MEC) has been developed by the International Institute for Applied Systems Analysis (IIASA) as an online tool to compare greenhouse gas (GHG) mitigation proposals by various countries for the year 2020. In this paper, first we introduce the MEC conceptual model, i.e. the methodology and system architecture. We then discuss the abstract formulation of four different international greenhouse gas trading regimes that are conceivable. Hereafter, the optimization process and its output results, namely cost curves are presented. Finally, we illustrate the MEC as a tool for interactively evaluating complex cost curve information in the context of GHG mitigation targets as currently discussed in international climate policy circles.

In this book techno-economic models are used to examine the cost-optimal future deployment of renewable power generation technologies as well as to gain methodological insights. This chapter introduces modeling concepts and methodologies required to construct these models, with main focus on economic models and methodologies. The first section covers general aspects and offers a brief introduction to decision theory, followed by an overview of equilibrium modeling approaches and their application to energy system modeling. The model class of mathematical programs and corresponding solution methods are then discussed in sections 3.3 and 3.4, respectively. Finally, section 3.5 is dedicated to uncertainty and stochastic modeling.

Cost reductions through technological progress are a key driver of renewable competitiveness. The main objective of this chapter is therefore to provide background on technological change in general with a specific focus on experience curves. After a general introduction of the topic in section 4.1, the concept of experience curves is introduced in section 4.2. The section covers the basic experience curve concept, drivers behind the cost reductions described by the experience curve, reasons for differences in learning rates among technologies, advanced experience curve concepts as well as the benefits and drawbacks of using experience curves as forecasting tool. A literature review to identify appropriate learning rates for renewable power generation technologies is then carried out in section 4.3. Section 4.4 then covers methods available to implement technological change in economic models and discusses options for dealing with non-convexity in bottom-up models. The chapter ends with a short discussion on how technological change is reflected in the models covered by this book and the benefits and pitfalls of this approach.

In this chapter the large-scale power sector model CORE is presented, which is required to address the research question raised in chapter 2. The chapter begins with a discussion of objectives, general approach and the current state of research. The CORE model formulation is then presented in section 6.2, followed by the key assumptions made in section 6.3. In section 6.4 model results are discussed and the chapter ends with a short summary of findings and their implications in section 6.5.

A major drawback of employing experience curves to model future technological change is the sensitivity of results to the rate of change, which in turn is typically associated with a high degree of uncertainty. In this chapter a model is developed and analytically solved to examine properties of models with endogenous technological change under uncertainty. It has three main objectives: illustrate specific properties of stochastic programs with experience curves, examine whether technological diversification is an optimal strategy in presence of experience curves and uncertainty, and provide a tool for decision making support. After a review of objectives, approach and current state of research in section 5.1, the basic model is developed in section 5.2 and successively expanded in sections 5.3 and 5.4. In section 5.5 several assumptions required for the preceding sections are critically reviewed. The chapter closes with an illustrative application of the model to two renewable technologies in section 5.6 and a discussion of key findings and implications in section 5.7.

Instead of incorporating all complex related information system models which are relevant for different related aspects into one super-model, a multi-model framework that addresses those aspects in more detail has been studied. In this paper, first the framework is specified in term of a multidimensional data model and designed by using UML (Unified Modeling Language). The designed diagram also presents different related models as well as the interactive linkings among them. In this context, an instance of just-defined multi-model framework of well-established sectoral models, namely EC4MACS, has been developed to produce a comprehensive assessment of the likely development of future air quality and greenhouse gas emissions in Europe. The extracted-transformed-linked and published EC4MACS key data are represented in this paper in term of case studies.

EC4MACS (European Consortium for Modelling of Air Pollution and Climate Strategies) establishes a suite of modelling tools for a comprehensive integrated assessment of the effectiveness of emission control strategies for air pollutants and greenhouse gases. This assessment brought together expert knowledge in the fields of energy, transport, agriculture, forestry, land use, atmospheric dispersion, health and vegetation impacts, and it developed a coherent outlook into the future options to reduce atmospheric pollution in Europe. In this paper, first we introduce background to the EC4MACS framework, which links well-established sectoral models of the most relevant policy areas. In this context, an ETL package is used to gather extracted information from multiple model data sources. The integrated data are loaded into the GAINS (Greenhouse gas-Air pollution Interactions and Synergies) Data Warehouse. Afterwards, a web service based toolbox is developed to publish EC4MACS key data, which are represented in this paper in term of case studies.

The GAINS (Greenhouse gas - Air pollution Interactions and Synergies) model quantifies the full DPSIR (demand-pressure-state-impact-response) chain for the emissions of air pollutants and greenhouse gases. To fulfill regional specific requirements of the GAINS model, we have studied and developed a cloud intelligent service system for calculating emissions and costs for reducing emissions at regional as well as global levels. In this paper, first we present a cloud intelligent conceptual model that is used to specify an application framework, namely GAINS cloud intelligent application framework. Using this application framework, first we build a global data warehouse called GAINS DWH World, then a class of regional data warehouses, e.g. GAINS DWH Europe, GAINS DWH Asia, etc, are specified and used for regional data analysis and cost optimization.

Mitigating greenhouse gases (GHGs) is key to reducing the longterm impacts of climate change. In this paper we present the GAINS system, i.e. a data warehouse with an online integrated assessment model that is already used in various international policy fora as a tool to quantify the costs and environmental benefits of reducing emissions of greenhouses gases and air pollutants. We explain the basic concepts and requirements of the system and illustrate how short-term co-benefits for local pollution can motivate GHG mitigation as a response of the otherwise intangible long-term and global risk of climate change. Hereafter, GAINS can be used as a common framework to make available and to compare the implications of the outputs of different energy system models working at different spatial and temporal scales. Furthermore, outputs of GAINS can be used as input for other models. Finally, we thus illustrate how integrated data management as implemented in the GAINS system supports the development of science-driven policies in term of case studies.

The Mitigation Efforts Calculator (MEC) has been developed by the International Institute for Applied Systems Analysis (IIASA) as an online tool to compare greenhouse gas (GHG) mitigation proposals by various countries for the year 2020. In this paper, first we introduce the MEC conceptual model, i.e. the methodology and system architecture. Hereafter, the optimization process and its output results, namely cost curves are presented. We then discuss the abstract formulation of four different international greenhouse gas trading regimes that are conceivable. Finally, we illustrate the MEC as a tool for interactively evaluating complex cost curve information in the context of GHG mitigation targets as currently discussed in international climate policy circles.

The isomorphism problem for planar graphs is known to be efficiently solvable. For planar 3-connected graphs, the isomorphism problem can be solved by efficient parallel algorithms, it is in the class $AC^1$. In this paper we improve the upper bound for planar 3-connected graphs to unambiguous logspace, in fact to $UL cap coUL$. As a consequence of our method we get that the isomorphism problem for oriented graphs is in $NL$. We also show that the problems are hard for $L$.

Each federated data warehousing system contains a data warehouse and a class of data marts. Building and maintaining such systems are painful processes. In this paper, we propose our approach to improve these processes. First, a cloud-based federated data warehousing system architecture, description of which is specified in a formal manner, is introduced. Afterwards, Unified Modelling Language (UML) is used to design the conceptual model. In this context, a cloud-based federated data warehousing application framework and its components, i.e., global data warehouse and linked virtual data marts, as well as its services, i.e., platform-as-a-services, software-as-a-services are modelled. Hereafter, the GAINS model is presented as an implementation example to proof of our concepts.