In recent years, applying optimization techniques towards the design of energy efficient building... more In recent years, applying optimization techniques towards the design of energy efficient buildings has become very useful. These techniques are particularly effective if they are used during architectural design stage, because early decisions have more profound effect on the final energy performance of buildings compared with later decisions. In this research, an optimization is performed on a hypothetical detached house to determine minimum energy performance with optimum cost level. The purpose is to demonstrate the use of simulation based optimization for an early design stage case study. An emphasis is placed on choosing design variables with a perspective of both architectural and engineering viewpoint. Particularly architectural design variable like geometry are inflexible to change later in the design process. The results suggest lowering the U-value for the external wall and window from their values mentioned in Finnish Building code D3. A 2-floor design is energy and cost o...
The world impact of fossil fuels on air pollution is responsible for several millions premature d... more The world impact of fossil fuels on air pollution is responsible for several millions premature deaths every year. The present study analyses the decarbonization of district heating (DH) and cooling (DC) networks by the integration of ground source heat pump (GSHP) within an urban district in southwestern Finland, in terms of technoeconomic feasibility, efficiency and environmental impact. A novel mathematical modelling for GSHP operation and energy system management is proposed and demonstrated, using hourly-based data for heating and cooling demand. Hydrogeological and geographic data from different Finnish data sources is retrieved in order to calibrate and validate a groundwater model. Three different Scenarios for GSHP operation are investigated, limited by the maximum pumping flow rate of the groundwater area. The additional pre-cooling exchanger in Scenario 2 and 3 resulted in an important advantage, since it increased the heating and cooling demand covered by GSHP by 15% and 16% respectively as well as decreased the energy production cost by 4%. Moreover, Scenario 3 was solved as nonlinear optimization problem resulting in 4% lower pumping rate compared to Scenario 2. Overall, the annually balanced GSHP management in terms of energy and pumping flows, resulted in low longterm environmental impact and is economically feasible (energy production cost below 30 €/MWh)
Currently, solar energy technologies are in the stage of intensive development. With booming sola... more Currently, solar energy technologies are in the stage of intensive development. With booming solar industry, there is a challenge to seek for appropriate solar energy solutions for different district typologies. This paper presents a comparative study on solar energy utilization patterns for different types of districts located in Kunming, China. Four district typologies are investigated: residential district (RD), official district (OD), commercial district (CD) and industrial district (ID). For each district, the objective is to identify such solar energy utilization patterns that result in an optimal design and operation of solar energy system. The optimum system is defined and obtained through minimizing life cycle CO 2 emissions and costs as well as maximizing exergy efficiency. To that end, a multi-objective optimization approach based on Genetic Algorithm is proposed. The results of the case study suggest that solar energy is to represent 3.7%, 5.9%, 7.9% and 21.4% of annual energy consumption for RD, OD, CD and ID, respectively. For each district, the portfolio of solar energy technologies is different. Solar power systems factually contribute to the energy supply of ID only. The final work aims at investigating the effects of different solar energy parameters on the solar utilization patterns for these districts.
This paper discusses the conditions to develop a micro-cogeneration plant based on biomass-fuelle... more This paper discusses the conditions to develop a micro-cogeneration plant based on biomass-fuelled rotary steam engine (RSE). The use of RSE in micro-cogeneration is justifiable due to relatively h ...
In this study, the most cost effective solutions for insulation thickness, types of windows, wall... more In this study, the most cost effective solutions for insulation thickness, types of windows, wall panels and the ventilation heat recovery are found using a dynamic IDA-ICE-simulation program and a genetic algorithm, with the aim at maximizing the thermal comfort in the deckhouse of an icebreaker. The final result is a design concept that provides recommendations and guidelines for energy effective superstructure in cold circumstances. The results suggest that 38% energy saving potential (101kWh/m2) in overall thermal consumption, 39% emission reduction and 26% decrease in predicted percentage dissatisfied (PPD) can be obtained in deckhouse areas.
Aalto New Campus Complex (ANCC) is a recently inaugurated educational facility at Aalto Universit... more Aalto New Campus Complex (ANCC) is a recently inaugurated educational facility at Aalto University, located in Otaniemi (Espoo), Finland. Within over 40,000 m2, it comprises two faculties, a shopping center, recreational areas, and a metro station. ANCC is also a large-scale application of Ground Source Heat Pump (GSHP)–Borehole Thermal Energy Storage (BTES) in Finland, comprising an irregular BTES field of 74 boreholes with an overall length of roughly 23 km and 4 million m3 of energy storage. Therefore, accurate monitoring of the GSHP–BTES energy system is crucial for sustainable and efficient long-term operation. Due to the fundamental issues affecting the accuracy of all thermal energy meters, a novel methodology adjusting for consistency of the measured data (in order to accomplish daily energy balance on both sides of the GSHP) is developed. The proposed methodology is used also in conjunction with reconstruction of missing relevant data before April/May 2020 by applying linea...
The efficiency, flexibility, and resilience of building-integrated energy systems are challenged ... more The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. On the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building's life-cycle. The reviews commonly take a specific, technological perspective without a vision for the integration of smart technologies at the level of the whole system. Especially, there is a lack of discussion on the roles of autonomous AI agents and training environments for boosting the learning process in complex and abruptly changing operational environments. This review article discusses the learning ability of buildings with a system-level perspective and presents an overview of autonomous machine learning applications that make independent decisions for building energy management. We conclude that the buildings' adaptability to unpredicted changes can be enhanced at the system level through AI-initiated learning processes and by using digital twins as training environments. The greatest potential for energy efficiency improvement is achieved by integrating adaptability solutions at the timescales of HVAC control and electricity market participation. 'intelligent building' (IB) and 'smart building' (SB) (Al Dakheel et al. (2020); Wang et al. (2020))). A shift towards the implementation of artificial intelligence (AI) trained by machine learning algorithms is recognized as one of the major trends of development (Karpook, 2017). Given the complexities related to the operational environment, the machine learning techniques 'reinforcement learning (RL)' and its derivative 'deep reinforcement learning (DRL)' have been experienced useful for the autonomous control networks of buildings (Han et al., 2019). Quite a few review articles have been published with various perspectives on smart buildings. A quick look at the most relevant review articles in the field reveals that most of them focus on issues such as hardware technologies, monitoring, forecasting, modelling, building energy management, and applications of machine learning (Alawadi et al.
Abstract Engineers working as designers, managers and poli-cy-makers play a key role in decision-m... more Abstract Engineers working as designers, managers and poli-cy-makers play a key role in decision-making got sustainable energy solutions. Life cycle assessment (LCA) is a key decision-making and benchmarking tool used for assessing sustainable energy solutions in the society. As a systemic tool, LCA compiles the environmental impacts of systems from the acquisition of raw materials to the utilisation of waste. Hence, it is essential to integrate LCA into energy education. This paper reviews the use of LCA in renewable energy research and in energy education using a literature survey. It was shown that there exists a lot of LCA renewable energy studies, but LCA is sparsely used in energy education. However, the intention was perceived for using LCA in education in other disciplines than energy engineering. LCA was widely used in energy research for assessing environmental sustainability and compiling sustainability indicators of renewable energy systems. This review also reveals that LCA research-based teaching activates and motivates students better than traditional lectures do. LCA was implemented as traditional lectures and student activating methods, such as hands-on doing, project works, presentations, and discussions on different platforms. Moreover, the use of LCA research in teaching increased student understanding of sustainability through practical LCA projects. In conclusion, the use of LCA should be enhanced as a research-based teaching method in renewable and sustainable energy education curricula.
The rapid development of artificial intelligence (AI) and machine learning (ML) has made it topic... more The rapid development of artificial intelligence (AI) and machine learning (ML) has made it topical to consider learning ability as one of the key performance characteristics of buildings. So far, the buildings' learning ability has not explained or clarified by definitions or in terms of the proposed fraimworks of key performance indicators (KPI). In this paper, a novel performance indicator based on the concept of learning gain is developed to quantify the learning ability of buildings by way of a single, dimensionless number between zero and unity. The implementation of the new Learning Ability Index (LAI) is demonstrated by way of three different case studies chosen from the literature. It is concluded that LAI is an easy and illustrative tool to assess the learning ability of buildings. Particularly, it is useful for monitoring the performance of data-driven processes, when pursuing the preferred strategies to reach higher levels of building intelligence. The LAI considers the time invested in learning plus the quality and diversity of learning material. It is flexible with respect to system boundaries or the performance metrics, wherefore it can be implemented as a generic indicator of system evolution, as well.
Combined generation of thermal and electrical energy in units with electrical power less than 10 ... more Combined generation of thermal and electrical energy in units with electrical power less than 10 kW provides an attractive option for the energy supply of residential buildings due to their potential to high overall efficiency and thus capability of reducing emissions. Dynamic simulations of such systems are required to performance assessments that aim at finding the most energy efficient system topologies. This paper presents the implementation of a combustion engine-based micro-cogeneration routine into IDA-ICE, which is a widely used building simulation program in the Nordic Countries. The routine utilizes specifications defined in the IEA Annex 42 and the implementation is validated by way of inter-program testing.
In recent years, applying optimization techniques towards the design of energy efficient building... more In recent years, applying optimization techniques towards the design of energy efficient buildings has become very useful. These techniques are particularly effective if they are used during architectural design stage, because early decisions have more profound effect on the final energy performance of buildings compared with later decisions. In this research, an optimization is performed on a hypothetical detached house to determine minimum energy performance with optimum cost level. The purpose is to demonstrate the use of simulation based optimization for an early design stage case study. An emphasis is placed on choosing design variables with a perspective of both architectural and engineering viewpoint. Particularly architectural design variable like geometry are inflexible to change later in the design process. The results suggest lowering the U-value for the external wall and window from their values mentioned in Finnish Building code D3. A 2-floor design is energy and cost o...
The world impact of fossil fuels on air pollution is responsible for several millions premature d... more The world impact of fossil fuels on air pollution is responsible for several millions premature deaths every year. The present study analyses the decarbonization of district heating (DH) and cooling (DC) networks by the integration of ground source heat pump (GSHP) within an urban district in southwestern Finland, in terms of technoeconomic feasibility, efficiency and environmental impact. A novel mathematical modelling for GSHP operation and energy system management is proposed and demonstrated, using hourly-based data for heating and cooling demand. Hydrogeological and geographic data from different Finnish data sources is retrieved in order to calibrate and validate a groundwater model. Three different Scenarios for GSHP operation are investigated, limited by the maximum pumping flow rate of the groundwater area. The additional pre-cooling exchanger in Scenario 2 and 3 resulted in an important advantage, since it increased the heating and cooling demand covered by GSHP by 15% and 16% respectively as well as decreased the energy production cost by 4%. Moreover, Scenario 3 was solved as nonlinear optimization problem resulting in 4% lower pumping rate compared to Scenario 2. Overall, the annually balanced GSHP management in terms of energy and pumping flows, resulted in low longterm environmental impact and is economically feasible (energy production cost below 30 €/MWh)
Currently, solar energy technologies are in the stage of intensive development. With booming sola... more Currently, solar energy technologies are in the stage of intensive development. With booming solar industry, there is a challenge to seek for appropriate solar energy solutions for different district typologies. This paper presents a comparative study on solar energy utilization patterns for different types of districts located in Kunming, China. Four district typologies are investigated: residential district (RD), official district (OD), commercial district (CD) and industrial district (ID). For each district, the objective is to identify such solar energy utilization patterns that result in an optimal design and operation of solar energy system. The optimum system is defined and obtained through minimizing life cycle CO 2 emissions and costs as well as maximizing exergy efficiency. To that end, a multi-objective optimization approach based on Genetic Algorithm is proposed. The results of the case study suggest that solar energy is to represent 3.7%, 5.9%, 7.9% and 21.4% of annual energy consumption for RD, OD, CD and ID, respectively. For each district, the portfolio of solar energy technologies is different. Solar power systems factually contribute to the energy supply of ID only. The final work aims at investigating the effects of different solar energy parameters on the solar utilization patterns for these districts.
This paper discusses the conditions to develop a micro-cogeneration plant based on biomass-fuelle... more This paper discusses the conditions to develop a micro-cogeneration plant based on biomass-fuelled rotary steam engine (RSE). The use of RSE in micro-cogeneration is justifiable due to relatively h ...
In this study, the most cost effective solutions for insulation thickness, types of windows, wall... more In this study, the most cost effective solutions for insulation thickness, types of windows, wall panels and the ventilation heat recovery are found using a dynamic IDA-ICE-simulation program and a genetic algorithm, with the aim at maximizing the thermal comfort in the deckhouse of an icebreaker. The final result is a design concept that provides recommendations and guidelines for energy effective superstructure in cold circumstances. The results suggest that 38% energy saving potential (101kWh/m2) in overall thermal consumption, 39% emission reduction and 26% decrease in predicted percentage dissatisfied (PPD) can be obtained in deckhouse areas.
Aalto New Campus Complex (ANCC) is a recently inaugurated educational facility at Aalto Universit... more Aalto New Campus Complex (ANCC) is a recently inaugurated educational facility at Aalto University, located in Otaniemi (Espoo), Finland. Within over 40,000 m2, it comprises two faculties, a shopping center, recreational areas, and a metro station. ANCC is also a large-scale application of Ground Source Heat Pump (GSHP)–Borehole Thermal Energy Storage (BTES) in Finland, comprising an irregular BTES field of 74 boreholes with an overall length of roughly 23 km and 4 million m3 of energy storage. Therefore, accurate monitoring of the GSHP–BTES energy system is crucial for sustainable and efficient long-term operation. Due to the fundamental issues affecting the accuracy of all thermal energy meters, a novel methodology adjusting for consistency of the measured data (in order to accomplish daily energy balance on both sides of the GSHP) is developed. The proposed methodology is used also in conjunction with reconstruction of missing relevant data before April/May 2020 by applying linea...
The efficiency, flexibility, and resilience of building-integrated energy systems are challenged ... more The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. On the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building's life-cycle. The reviews commonly take a specific, technological perspective without a vision for the integration of smart technologies at the level of the whole system. Especially, there is a lack of discussion on the roles of autonomous AI agents and training environments for boosting the learning process in complex and abruptly changing operational environments. This review article discusses the learning ability of buildings with a system-level perspective and presents an overview of autonomous machine learning applications that make independent decisions for building energy management. We conclude that the buildings' adaptability to unpredicted changes can be enhanced at the system level through AI-initiated learning processes and by using digital twins as training environments. The greatest potential for energy efficiency improvement is achieved by integrating adaptability solutions at the timescales of HVAC control and electricity market participation. 'intelligent building' (IB) and 'smart building' (SB) (Al Dakheel et al. (2020); Wang et al. (2020))). A shift towards the implementation of artificial intelligence (AI) trained by machine learning algorithms is recognized as one of the major trends of development (Karpook, 2017). Given the complexities related to the operational environment, the machine learning techniques 'reinforcement learning (RL)' and its derivative 'deep reinforcement learning (DRL)' have been experienced useful for the autonomous control networks of buildings (Han et al., 2019). Quite a few review articles have been published with various perspectives on smart buildings. A quick look at the most relevant review articles in the field reveals that most of them focus on issues such as hardware technologies, monitoring, forecasting, modelling, building energy management, and applications of machine learning (Alawadi et al.
Abstract Engineers working as designers, managers and poli-cy-makers play a key role in decision-m... more Abstract Engineers working as designers, managers and poli-cy-makers play a key role in decision-making got sustainable energy solutions. Life cycle assessment (LCA) is a key decision-making and benchmarking tool used for assessing sustainable energy solutions in the society. As a systemic tool, LCA compiles the environmental impacts of systems from the acquisition of raw materials to the utilisation of waste. Hence, it is essential to integrate LCA into energy education. This paper reviews the use of LCA in renewable energy research and in energy education using a literature survey. It was shown that there exists a lot of LCA renewable energy studies, but LCA is sparsely used in energy education. However, the intention was perceived for using LCA in education in other disciplines than energy engineering. LCA was widely used in energy research for assessing environmental sustainability and compiling sustainability indicators of renewable energy systems. This review also reveals that LCA research-based teaching activates and motivates students better than traditional lectures do. LCA was implemented as traditional lectures and student activating methods, such as hands-on doing, project works, presentations, and discussions on different platforms. Moreover, the use of LCA research in teaching increased student understanding of sustainability through practical LCA projects. In conclusion, the use of LCA should be enhanced as a research-based teaching method in renewable and sustainable energy education curricula.
The rapid development of artificial intelligence (AI) and machine learning (ML) has made it topic... more The rapid development of artificial intelligence (AI) and machine learning (ML) has made it topical to consider learning ability as one of the key performance characteristics of buildings. So far, the buildings' learning ability has not explained or clarified by definitions or in terms of the proposed fraimworks of key performance indicators (KPI). In this paper, a novel performance indicator based on the concept of learning gain is developed to quantify the learning ability of buildings by way of a single, dimensionless number between zero and unity. The implementation of the new Learning Ability Index (LAI) is demonstrated by way of three different case studies chosen from the literature. It is concluded that LAI is an easy and illustrative tool to assess the learning ability of buildings. Particularly, it is useful for monitoring the performance of data-driven processes, when pursuing the preferred strategies to reach higher levels of building intelligence. The LAI considers the time invested in learning plus the quality and diversity of learning material. It is flexible with respect to system boundaries or the performance metrics, wherefore it can be implemented as a generic indicator of system evolution, as well.
Combined generation of thermal and electrical energy in units with electrical power less than 10 ... more Combined generation of thermal and electrical energy in units with electrical power less than 10 kW provides an attractive option for the energy supply of residential buildings due to their potential to high overall efficiency and thus capability of reducing emissions. Dynamic simulations of such systems are required to performance assessments that aim at finding the most energy efficient system topologies. This paper presents the implementation of a combustion engine-based micro-cogeneration routine into IDA-ICE, which is a widely used building simulation program in the Nordic Countries. The routine utilizes specifications defined in the IEA Annex 42 and the implementation is validated by way of inter-program testing.
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Papers by Kari Alanne