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With increasing concern over global climate change and rapid rise in energy consumption on the data-driven market, data centres are an important renewable energy target. As cooling contributes a substantial portion of the energy use of data centres, minimising the cooling energy demand in data centres is one of the main objectives for improving renewable energy. This paper investigates overall energy consumption and the energy efficiency of the cooling system for a data centre in Finland as a case study. The temporal energy consumption characteristics, cooling infrastructure and operation of the data centre are analysed. The main problems associated with cooling energy efficiency and the factors that may contribute toward higher efficiency are identified and further suggestions are put forward. Results are presented of an extensive evaluation of the energy performance of the study data centre with a view to energy recovery. The conclusion we can draw is that even though the analysed data centre demonstrat...

The building sector contributes up to 30% of global annual greenhouse gas emissions and consumes up to 40% of all energy. Failure to encourage energy-efficiency and low-carbon in new builds or retrofitting will lock countries into the disadvantages of poor performing buildings for decades. The journey towards low-carbon and energy efficient buildings starts with good design, commissioning and measuring. The share of energy costs can be up to 50% of all maintenance costs [7] in Finland. In the studied buildings the average costs were 39% for daycare centres and 45% for schools. Since the share of energy costs is remarkable in maintenance, it is important to find out the most concrete indicators to measure energy efficiency in practice. This study explores ways in which building usage and occupancy influences the energy cost in Finnish daycare centres and school buildings. This study shows that energy costs vary a lot between different energy efficiency indicators, i.e. there is great variation in energy costs regardless of the building age and when child or student density varies. Results indicated that actual use of space is profiled in the operational phase where the energy costs variation is remarkable.

Abstract The aim of this review is to discuss how quantitative modelling of energy scenarios for sustainable energy transition pathways can be made more realistic by taking into account insights from the socio-technical and related literatures. The proposition is that an enriched modelling approach would focus not just on technology development and deployment, but also on feedback loops, learning processes, policy and governance, behavioural changes, the interlinkages between the energy sector and other economic sectors, and infrastructure development. The review discusses a range of socio-technical concepts with a view to how they can enrich the understanding and modelling of highly complex dynamic systems such as flexible energy systems with high shares of variable renewable energy. In this context, application of system dynamics modelling (SDM) for the analysis of energy transitions is also introduced by describing the differences between SDM and a traditional modelling approach that uses econometric and linear programming methods. A conceptual framework for this type of modelling is provided by using causal loop diagrams. The diagrams illustrate the endogenous approach of SDM – understanding and modelling the structure of a system, which is responsible for its dynamic behaviour. SDM can also capture the co-evolution of economic, policy, technology, and behavioural factors over sufficiently long time periods, which is necessary for the analysis of transition pathway dynamics. In this regard, the review summarises how socio-technical concepts can be approached in SDM and why they are relevant for the analysis of flexibility in energy systems. From a computational point of view, it could be beneficial to combine SDM with technologically detailed energy system optimization models and that could be a way forward for achieving more realistic, non-linear quantitative modelling of sustainable energy transitions.

Abstract Energy costs in the wastewater industry are increasing due to increasing trends in electricity rates and more stringent requirements for effluent quality. Wastewater aeration process is typically the largest energy consumer of the treatment plant and the optimization of the aeration process can offer significant savings for the wastewater treatment plants (WWTPs). Utilization of dynamic models can offer optimization solutions for improving the energy efficiency and process performance. In this work a simplified modelling approach emphasizing the control valves and the blowers is tested by developing aeration system models for two Finnish WWTPs. The developed model requires calibration of only a single parameter and the results from the simulations showed that reasonable estimations of the aeration systems energy demand could be made with a limited knowledge on the details of the physical system. The promising results highlight the strong influence of the control valve positioning to the whole system and indicate that airflow distribution along the system could be estimated simply from the positioning of the valves. The presented modelling approach allows the comparison between different blower and control valve alternatives during operation and for the process upgrades and offers prospect for improving the aeration operation control strategies.

Buildings account for nearly 50% of all greenhouse gases globally. While this has been widely recognized, the GHG mitigation strategies have traditionally concentrated on reducing the use phase emissions, as over 90% of the emissions are generated during the use phase according to several studies. However, two current developments increase the importance of the construction phase emissions and the embodied emissions of the building materials. Firstly, the improvements in the energy efficiency of buildings directly increase the relative share of the construction phase emissions. Secondly, the notification of the temporal allocation of the emissions increases the importance of the carbon spike from construction. While these perspectives have been noted, few studies exist that combine the two perspectives of the construction and the use phase. In this paper, we analyze the implications of low-carbon residential construction on the life cycle emissions of a residential area with a case study. Furthermore, we demonstrate that when the temporal allocation of the emissions is taken into account, the construction phase emissions can hinder or even reverse the carbon mitigation effect of low-carbon buildings for decades.

The growing demand for methanol as fuel and global competition for resources are key drivers behind the need to find new routes for the production of bulk chemicals such as methanol. Widening the resource base is also linked to the increasing concentrations of methane in the atmosphere. Furthermore, managing greenhouse gas emissions is vital in developing new technologies. This paper compares production routes for methanol based on a cradle-to-gate life cycle assessment (LCA). The LCA is limited to the impact categories of global warming potential (GWP100) and energy use. The highest GWP100 value of 2.97 kg CO2eq/kg CH3OH is for methanol from coal, and the lowest, negative emission of 0.99 kg CO2eq/kg CH3OH is for methanol in co-production with renewable corn ethanol. A comparison of production routes is performed using the carbon dioxide equivalent abatement cost, and the production cost of methanol. The best performing technology on both production cost and GWP100 is methanol produced by gasification from wood biomass. The factors affecting the results are addressed. >

Abstract We consider domestic hot water (DHW) consumption hourly data for Finnish apartments in November and August. Using datasets obtained in a previous work, we formulate a bottom-up model to quantify correlations in the consumption patterns, which are discerned by a different number of occupants for both weekday (WD) and weekend (WE). The analytical formulas thus obtained describe accurately the hourly consumption of any specific dataset. In particular, we can generate the consumption curves for unknown datasets and derive quantitatively the correlations between occupant groups and different seasons. We explain this procedure into details, define the key variables of the model and validate it against the measurements. Our quantitative results are immediately applicable to simulation tools for energy investigations and sizing of heating systems in Finland or areas with similar occupant behavior. More generally, the analytical, inductive method here introduced could be adapted to DHW studies concerning other geographic areas as well. We also argue that this simple, yet effective formalism might also be extended to other engineering contexts that are not strictly related to energy consumption. For example, the main idea could be developed and adapted to those disciplines where understanding dataset correlations constitutes an important investigation tool.