Jean-Louis Scartezzini’s research while affiliated with Swiss Federal Institute of Technology in Lausanne and other places

Households represent a large share of flexibility on the demand side for assisting the smooth operation of the highly renewable grid. The stochastic nature of activities and various appliance usage patterns of household occupants affects the flexibility potential greatly, but is usually oversimplified in the existing research. This paper aims to propose a novel method for simulating the dynamics of household energy-related activities and appliance usage, which provides realistic synthetic load profiles for smart energy management. To this aim, a highly resolved multi-agent system model is proposed, which comprises an Agent-Based Activity Chain Model (ABACM) and 34 types of common household appliance models. Firstly, the patterns of various occupant behaviors are obtained through mining of real-world residential time-of-use data. Then, the stochastic activity profiles of occupants are generated using the ABACM, with a root mean square error of 0.95%. Finally, the electricity consumption profiles of household appliances are simulated based on specific energy-related activities. The proposed method is validated and proved to be able to capture stochastic occupant behaviors and represent the dynamics of residential energy consumption. The future work will focus on using the proposed model for exploring the potential of residential demand response.

The growth of wind generation capacities in the past decades has shown that wind energy can contribute to the energy transition in many parts of the world. Being highly variable and complex to model, the quantification of the spatio-temporal variation of wind power and the related uncertainty is highly relevant for energy planners. Machine Learning has become a popular tool to perform wind-speed and power predictions. However, the existing approaches have several limitations. These include (i) insufficient consideration of spatio-temporal correlations in wind-speed data, (ii) a lack of existing methodologies to quantify the uncertainty of wind speed prediction and its propagation to the wind-power estimation, and (iii) a focus on less than hourly frequencies. To overcome these limitations, we introduce a framework to reconstruct a spatio-temporal field on a regular grid from irregularly distributed wind-speed measurements. After decomposing data into temporally referenced basis functions and their corresponding spatially distributed coefficients, the latter are spatially modelled using Extreme Learning Machines. Estimates of both model and prediction uncertainties, and of their propagation after the transformation of wind speed into wind power, are then provided without any assumptions on distribution patterns of the data. The methodology is applied to the study of hourly wind power potential on a grid of $250\times 250$ m$^2$ for turbines of 100 meters hub height in Switzerland, generating the first dataset of its type for the country. The potential wind power generation is combined with the available area for wind turbine installations to yield an estimate of the technical potential for wind power in Switzerland. The wind power estimate presented here represents an important input for planners to support the design of future energy systems with increased wind power generation.

Enhancing lighting conditions in institutions for individuals with dementia improves their sleep, circadian rhythms and well-being. Here, we tested whether a greater long-term daily light exposure supports the immune response to the annual influenza vaccination. Eighty older institutionalised patients suffering from dementia (54 women and 26 men) continuously wore an activity tracker for 8 weeks to assess individual light exposure and rest-activity cycles. The patients’ immune response was analysed from two blood samples taken before and 4 - 5 weeks after the annual influenza vaccination. Individual antibody concentrations to three influenza virus strains (H3N2, H1N1, IB) were quantified via hemagglutination inhibition assays. By quantifying individual light exposure profiles (including daylight), we classified the patients into a low and a high light exposure group based on a median illuminance of 392.6 lux. The two light exposure groups did not differ in cognitive impairment severity, age or gender distribution. However, patients in the high light exposure group showed a significantly greater circadian rest-activity amplitude (i.e. more daytime activity and less nighttime activity) along with a significantly greater antibody titer increase to the H3N2 vaccine than patients in the low light exposure group, despite similar pre-vaccination concentrations. Sufficient seroprotective responses to all three influenza virus strains were attained for ≥ 75 % of participants. These data provide first evidence for an enhanced immune response in patients with dementia when they received more daily light. Increasing daily light exposure may have beneficial effects on the human immune system, either directly or via circadian rhythm stabilisation.

In developing countries with a large population and fast urbanization, High-rise Residential Buildings (HRBs) have unavoidably become a very common, if not the most, accommodation solution. The paradigm of HRB energy consumption is characterized by high-density energy consumption, severe peak effects and a limited site area for integrating renewable energy, which constitute a hindrance to the low-carbon transition. This review paper investigates low-carbon transition efforts in the HRB sector from the perspective of urban energy systems to get a holistic view of their approaches. The HRB sector plays a significant role in reducing carbon emission and improving the resilience of urban energy systems. Different approaches to an HRB low-carbon transition are investigated and a brief overview of potential solutions is offered from the perspectives of improving energy efficiency, self-sufficiency and system resilience. The trends of decarbonization, decentralization and digitalization in the HRB sector allow a better alignment with transitioning urban energy systems and create cross-sectoral integration opportunities for low-carbon transition. It is also found that policy tools are powerful driving forces in China for incentivizing transition behaviors among utilities, end users and developers. Based on a comprehensive policy review, the policy implications are given. The research is geared for the situation in China but could also be used as an example for other developing countries that have similar urbanization patterns. Future research should focus on quantitative analysis, life-cycle analysis and transdisciplinary planning approaches.

In order to achieve the climate goal of net zero greenhouse gas emissions in 2050, there is an urgent need to transform the current energy system. The pace of energy efficiency improvement on the demand side must be accelerated. However, this strategy alone is not sufficient. Today's energy consumers must be supplied exclusively with CO2-free energy in future. This will lay the foundations for all sectors of the economy and for the consumers to work, act, and live in a climate-friendly manner. Such a transformation of the energy system will take place in parallel with the exploitation of local renewable energy sources, such as solar, wind, geothermal, lakes, and others, which are mostly small-scale and distributed in urban and rural areas. The Swiss Competence Center for Energy Research in Buildings and Districts, SCCER FEEB&D, has conducted research over the past 7 years in the field of energy supply and demand for buildings, districts and cities. The energy consumption of the Swiss building stock for space heating, space cooling, electricity and hot water amounts to approximately 37% of the total final energy consumption in Switzerland and accounts for around 27% of Swiss domestic CO2,eq emissions. Our research reveals that the net-zero target for the building sector can be achieved by 2050. The transformation can be implemented with economically attractive solutions, assuming CO2 avoidance costs of 200-400 CHF per ton of CO2. The economic attractiveness depends on the type of building and the development of the overall renewable energy supply. The research also shows that transforming the energy system into a renewable system increases the security of energy supply and local value creation. In essence, the SCCER FEEB&D research identified six paradigm shifts that relate to the building stock and sup-port the transformation of the current into the future net-zero CO2-emisson energy system. The following explanations are not exhaustive, but are embedded seamlessly in further paradigm shifts from other research or in remaining paradigms.

The thermal performance gap in buildings is defined as the difference between the theoretical and the actual energy consumption for heating, and is known to undermine energy retrofit strategies and policies. This study examines the performance gap in retrofitted buildings using the Swiss Cantonal Energy Certificate for Buildings (CECB) database, using a sample of 1172 buildings for which both theoretical and actual metered consumption were known. We found an average negative performance gap of -23% for pre-retrofit buildings (actual consumption smaller than calculated) and instead a good approximation of actual consumption with theoretical consumption after retrofitting (a positive gap of 2%). A regression analysis on the energy performance certificate input parameters characterizing the building led to the conclusion that these are poor predictors of actual consumption compared to the theoretical calculation: parameters such as the energy label and the thermal proprieties of the envelope (U-values) have minor explanatory power for the actual consumption despite explaining a high degree of change in the theoretical consumption. Analysis of the indicator Energy Savings Deficit (ESD) shows an overestimation (of 37%) of the achievable savings on the basis of the theoretical consumption, whereas the prediction of savings using measured consumption before retrofit resulted in a good agreement with the actual savings (3.6% overestimation). This implies that energy savings can be estimated rather accurately by comparing the actual current consumption with the expected theoretical consumption defined by the certificate after retrofit.

Rule-based control logics have proven to be efficient for automation of shading and lighting systems. However, successful commissioning of these systems for multiple stores non-residential buildings, regardless of the control logic, requires time-consuming programing and fine-tuning of numerous parameters. In this article, a control approach is suggested to overcome the limitations of the rule-based control systems: i) necessity for extensive information on the office room, ii) costly adaptation of control parameters to a new environment, and iii) no feedback to the control system in the case of mal-function. A novel self-commissioning approach is proposed: a set of open-loop geometry-based rules are enhanced with a supervised learning module for fine-tuning seven tunable parameters. This approach was validated through an in-situ experiment for 22 days in a daylight testbed equipped with internal venetian blinds, dimmable electric lighting system, a miniaturized accurate High Dynamic Range vision sensor to evaluate Daylight Glare Probability (DGP), horizontal illuminance meters and a pyranometer. The goal was to command the shading and electric lighting system to keep the DGP and horizontal illuminance in a predefined visual comfort zone. A novel visualization method is proposed to demonstrate the performance of the automatic system in leading the indoor environment to the visual comfort zone. After 11 days, the learning module reached the convergence state. Afterwards, the controller was capable of successfully confining the indoor illumination conditions to the visual comfort zone for 96% of the working hours while actuating the shading system efficiently; on average 2.54 times a day.

Design optimization of distributed energy systems has become an interest of a wider group of researchers due the capability of these systems to integrate non-dispatchable renewable energy technologies such as solar PV and wind. White box models, using linear and mixed integer linear programing techniques, are often used in their design. However, the increased complexity of energy flow (especially due to cyber-physical interactions) and uncertainties challenge the application of white box models. This is where data driven methodologies become effective, as they demonstrate higher flexibility to adapt to different environments, which enables their use for energy planning at regional and national scale. This study introduces a data driven approach based on reinforcement learning to design distributed energy systems. Two different neural network architectures are used in this work, i.e. a fully connected neural network and a convolutional neural network (CNN). The novel approach introduced is benchmarked using a grey box model based on fuzzy logic. The grey box model showed a better performance when optimizing simplified energy systems, however it fails to handle complex energy flows within the energy system. Reinforcement learning based on fully connected architecture outperformed the grey box model by improving the objective function values by 60%. Reinforcement learning based on CNN improved the objective function values further (by up to 20% when compared to a fully connected architecture). The results reveal that data-driven models are capable to conduct design optimization of complex energy systems. This opens a new pathway in designing distributed energy systems.