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In the light of new European energy-climate package and its measures for increasing security of supply, decreasing the impact on environment and stimulating sustainable development with special emphasis on job creation and regional growth, Croatia as a future EU member state, needs to reconsider and develop new energy policy towards energy efficiency and renewable energy sources, in order to comply with all of the presented tasks. Planning future energy demand, considering various policy options like regulation, fiscal and financial measures, becomes one of the crucial issues of future national energy strategy. This paper analyses Croatian long term energy demand and its effect on the future national GHG emissions. For that purpose the national energy demand model was constructed (NeD model). The model is comprised out of six modules each representing one sector, following Croatian national energy balance; industry, transport, households, services, agriculture and construction. For three of the modules (industry, transport and households) previously developed long term energy demand models were used, while for the remaining three new models were constructed. As an additional feature, new GHG module was assembled and added to the NeD model covering all six sectors and calculating its emissions. The model is based on bottom up approach, where data was available, which combines and process large number of input data at the end use level. NeD model was used to present different future GHG mitigation paths, based on the implementation of various energy policies. Results obtained in this paper were also compared to Croatian National Energy Strategy for the years 2020 and 2030. It was shown that if already implemented policies are properly taken into account the actual final energy demand for 2030 will be 43% lower than forecast by the Croatian National Energy Strategy.

To achieve the European Union's target for climate neutrality by 2050 reduced energy demand will make the transition process faster and cheaper. The role of policies that support energy efficiency measures and demand-side management practices will be critical and to ensure that energy demand models are relevant to policymakers and other end-users, understanding how to further improve the models and whether they are tailored to user needs to support efficient decision-making processes is crucial. So far though, no scientific studies have examined the key user needs for energy demand modelling in the context of the climate neutrality targets. In this article we address this gap using a multi-method approach based on empirical and desk research. Through survey and stakeholder meetings and workshops we identify user needs of different stakeholder groups, and we highlight the direction in which energy demand models need to be improved to be relevant to their users. Through a detailed review of existing energy demand models, we provide a full understanding of the key characteristics and capabilities of existing tools, and we identify their limitations and gaps. Our findings show that classical demand-related questions remain important to model users, while most of the existing models can answer these questions. Furthermore, we show that some of the user needs related to sectoral demand modelling, dictated by the latest policy developments, are under-researched and are not addressed by existing tools.

A thorough assessment of energy sources and long-term energy demand predictions are crucial to the development of sustainable energy planning and policy for any country. However, these are uncommon practices in Nigeria, thereby resulting in shortage of energy supplies required by the economy and its growing population. In this study, energy demand in the commercial and public services, industrial, and residential sectors of the Nigerian economy is examined using Autoregressive Conditional Heteroskedasticity, Markov Switching, and Unobserved Component models. The results of the comparative study show that Markov Switching model performs better than Autoregressive Conditional Heteroskedasticity and Unobserved Component Model for energy demand forecasting. The outcomes further suggest that the energy demand in Nigeria will grow at an average annual rate of less than 2% from 2018 to 2050 in all the three sectors investigated. In view of these findings, the Nigerian Government should have made effort to collaborate with relevant stakeholders, aiming at harnessing the abundant energy resources needed to meet the future energy consumption with a guarantee of energy security and sustainability in the country.

The importance of considering homogenous economic agents when estimating energy demand functions is recognized in the literature, but so far data availability problems have explained the prevalence of empirical analyses only at an aggregate level. Motivated by the goal of developing the new industrial module to be adopted by the UK government Department of Business, Energy and Industrial Strategy (BEIS) for their econometric Energy Demand Model, we propose the first cointegration analysis that provides evidence on energy demand elasticities with respect to economic activity and energy price at a disaggregated industrial level. While the average of our estimates are comparable to those of the existing literature on the industrial sector as a whole, we find that there is considerable heterogeneity in relation to the long-run impact of economic activity and energy price on energy consumption, as well as to the speed with which firms re-adjust their equilibrium demand of energy in response to economic shocks. Finally, we learn that long-run disequilibria are tackled through altering the level of energy consumption rather than economic activity, a conclusion that has important implications for policy analysis.

The importance of considering homogenous economic agents when estimating energy demand functions is recognized in the literature, but so far data availability problems have explained the prevalence of empirical analyses only at an aggregate level. Motivated by the goal of developing the new industrial module to be adopted by the UK government Department of Business, Energy and Industrial Strategy (BEIS) for their econometric Energy Demand Model, we propose the first cointegration analysis that provides evidence on energy demand elasticities with respect to economic activity and energy price at a disaggregated industrial level. While the average of our estimates are comparable to those of the existing literature on the industrial sector as a whole, we find that there is considerable heterogeneity in relation to the long-run impact of economic activity and energy price on energy consumption, as well as to the speed with which firms re-adjust their equilibrium demand of energy in response to economic shocks. Finally, we learn that long-run disequilibria are tackled through altering the level of energy consumption rather than economic activity, a conclusion that has important implications for policy analysis.

The demand side plays a pivotal role in order to understand the expanse of the whole energy system, especially when one considers the European Union's (EU) commitment to climate neutrality by 2050, in the context of which, energy demand needs to be reduced substantially. Therefore, the role of policies that support energy efficiency measures and demand-side management practices will be critical; however, the impacts of such policies can only be explored ex-ante using energy demand models. In this context, the energy demand models (EDMs) used in the Sustainable Energy Transitions Laboratory (SENTINEL) project provide yearly and hourly future demand profiles for each of the EU Member States. However, EDMs, including the ones used in SENTINEL, namely: BEVPO, DESSTINEE, DREEM, and HEB, are often criticized for not incorporating dynamic GDP, or socio-political dimensions. Having that in mind, the SENTINEL EDMs will be soft-linked with the QTDIAN toolbox from WP2 to incorporate storylines of different socio-political developments, while calculating the future energy demand. Furthermore, the SENTINEL EDMs use a linear economic (mainly GDP) projection as input data to calculate future energy demand profiles. However, the linear projection of economic input usually excludes market uncertainties, and hence, makes model outputs less realistic. To overcome this limitation in SENTINEL, EDMs will be also soft-linked with the macroeconomic model WEGDYN from WP5. This soft-linking will make the output of the SENTINEL EDMs much more realistic, and, hence, it is expected to increase their useability in decision-making. Thus, the demand profiles, more precisely the hourly and yearly demand profiles for the building, transport and industry sectors, will be used in WP4 as an input to calculate the energy balance in the context of the transition to climate neutrality in the EU. Finally, to make these exercises more policy relevant, all the aforementioned soft-linkages will take place for three case studies of different heterogeneous ...

Increasing interest in low carbon electro-thermal heating technologies, such as electric heat pumps and combined heat and power units, may in the near future significantly change the character of domestic electricity and gas demand profiles. To understand the impact of adoption of such technologies it is necessary to appreciate fully the multi-energy aspects of such technologies, and indeed of any other domestic energy service demands. Such holistic treatment is necessary to fully capture the effects of coincidence in various energy service demands within and across dwellings. To that end this paper introduces and details a high resolution domestic multi-energy model comprised of physically based space heating, domestic hot water (DHW), cooking and electrical appliance models. Given the potential for thermal storage in the heating system and building elements particular attention is given to the space heating model, which includes building, heat emitter, buffer tank and heating unit models, capturing the relevant inter-temporal dependencies, heat transfer rates and system inertia. The power and interest of the presented model is demonstrated through exploration of a selection of various possible applications, including amongst others: effect of input energy/heating technology substitution on consumption profiles of different energy vectors, effect of thermal inertia, and investigation of dwelling diversity. Additionally the importance of high granularity modelling is demonstrated. The presented model can be utilised by many actors involved in energy systems for various purposes. For example electricity distribution network operators for modelling of network peak demand, demand aggregators for estimation of potential demand side flexibility, government agencies for assessing incentive scheme costs, electricity retailers for understanding the impact of electro-thermal technology adoption upon their demand portfolio or potential heat network operators for understanding the demand for heat in an area.

Producción Científica ; The estimation of future energy demand is a key factor for the development of effective alternative policies towards a low carbon economy. This paper describes a novel method to estimate the energy demand in the new integrated assessment framework MEDEAS based on the projection of sectoral final energy intensities. The dynamic of each of the sectoral final energy intensity is broken down into (1) improvement in energy efficiency and (2) substitution of the final energy. The speed of changes in these factors depend on physical supply-demand unbalances in the market, climate mitigation and other energy saving policies and the perception of scarcity of the different economic agents. The simulated case studies in MEDEAS-World under the narrative of the Business-as-usual (BAU) scenario have allowed validating the model's robustness and showing the potentiality of its application. ; European Union's Horizon 2020 research and innovation programme under grant agreement No 691287 ; Ministerio de Economía, Industria y Competitividad (Project FJCI-2016-28833)

PUBLISHED ; Cardiff ; This paper presents a new methodology for characterising the energy performance of buildings suitable for city-scale, top-down energy modelling. Building properties that have the greatest impact on simulated energy performance were identified via a review of sensitivity analysis studies. The methodology greatly simplifies the description of a building to decrease labour and simulation processing overheads. The methodology will be used in the EU FP7 INDICATE project which aims to create a master-planning tool that uses dynamic simulation to facilitate the design of sustainable, energy efficient smart cities. ; This work was conducted as part of the INDICATE project under the European Union?s 7th Framework Programme for research under grant number 608775

This article analyses the existing possibilities for using Standard Statistical Methods and Artificial Intelligence Methods for a short-term forecast and simulation of demand in the field of telecommunications. The most widespread methods are based on Time Series Analysis. Nowadays, approaches based on Artificial Intelligence Methods, including Neural Networks, are booming. Separate approaches will be used in the study of Demand Modelling in Telecommunications, and the results of these models will be compared with actual guaranteed values. Then we will examine the quality of Neural Network models.

The International Energy Agency has repeatedly identified increased end-use energy efficiency as the quickest, least costly method of green house gas mitigation, most recently in the 2012 World Energy Outlook, and urges all governing bodies to increase efforts to promote energy efficiency policies and technologies. The residential sector is recognised as a major potential source of cost effective energy efficiency gains. Within the EU this relative importance can be seen from a review of the National Energy Efficiency Action Plans (NEEAP) submitted by member states, which in all cases place a large emphasis on the residential sector. This is particularly true for Ireland whose residential sector has historically had higher energy consumption and CO2 emissions than the EU average and whose first NEEAP targeted 44% of the energy savings to be achieved in 2020 from this sector. This thesis develops a bottom-up engineering archetype modelling approach to analyse the Irish residential sector and to estimate the technical energy savings potential of a number of policy measures. First, a model of space and water heating energy demand for new dwellings is built and used to estimate the technical energy savings potential due to the introduction of the 2008 and 2010 changes to part L of the building regulations governing energy efficiency in new dwellings. Next, the author makes use of a valuable new dataset of Building Energy Rating (BER) survey results to first characterise the highly heterogeneous stock of existing dwellings, and then to estimate the technical energy savings potential of an ambitious national retrofit programme targeting up to 1 million residential dwellings. This thesis also presents work carried out by the author as part of a collaboration to produce a bottom-up, multi-sector LEAP model for Ireland. Overall this work highlights the challenges faced in successfully implementing both sets of policy measures. It points to the wide potential range of final savings possible from particular policy measures and the resulting high degree of uncertainty as to whether particular targets will be met and identifies the key factors on which the success of these policies will depend. It makes recommendations on further modelling work and on the improvements necessary in the data available to researchers and policy makers alike in order to develop increasingly sophisticated residential energy demand models and better inform policy.