A comparative benchmarking study of multi-storey residential buildings in two periods of energy saving efforts: the 1980s and the 2010s

In 1990, Technological Institute (TI) in Denmark made a benchmarking study of 92 typical multi-storey buildings covering 23 000 dwellings. The study included measurement data from the 1970s and the years after the energy crises. This study showed that over a period of less than 20 years a significant reduction in energy consumption took place. In a new similar study, TI and Aalborg University have analysed 62 buildings covering 18 000 dwellings including measurement data from the last 20 years. This time, the data covers a period with an increasing focus on the carbon-emission impacts of energy consumption. As opposed to the first benchmarking study, the new 20-years study shows that the heat consumption has been almost constant over the last 20 years. This paper presents a comparative study of the two sets of measurements and evaluates energy saving efforts and individual building energy performance. Furthermore, the paper compares two different ways of deriving benchmarks from the data and demonstrates how utilizing change-point models/energy signature as opposed to the more traditional mean annual values per heated area, significantly increases the usability.

Bayesian Inference of Dwellings Energy Signature at National Scale: Case of the French Residential Stock

Cities take a central place in today’s energy landscape. Urban Buildings Energy Modeling (UBEM) is identified as a promising approach for energy planning and optimization in cities and districts. It generally relies on the use of Building Archetypes, i.e., simplified deterministic models for categorized building typologies. However, this implies large assumptions which may accumulate and induce significant bias on energy consumption estimates. In this work, we address this issue with static stochastic models whose parameters are inferred over national thermo-energy data using Bayesian Inference. We analyze inference results and validate them with a panel of standard indicators. Then, we provide comparative results with deterministic building archetypes and stock data from the TABULA European project. Comparisons between heat loss coefficients show relative coherence between building categories, but highlight some significant bias between both approaches. This bias is also shown in the comparative result of a Monte Carlo simulation using inferred stochastic models for a 10331 dwellings stock. In conclusion, inferred stochastic models show interesting insights over the French dwellings stock and potential for district energy simulation. All code and data involved in this study are released in an open repository.

Interaction of near-inertial waves with an anticyclonic vortex

Anticyclonic vortices focus and trap near-inertial waves so that near-inertial energy levels are elevated within the vortex core. Some aspects of this process, including the nonlinear modification of the vortex by the wave, are explained by the existence of trapped near-inertial eigenmodes. These vortex eigenmodes are easily excited by an initialwave with horizontal scale much larger than that of the vortex radius. We study this process using a wave-averaged model of near-inertial dynamics and compare its theoretical predictions with numerical solutions of the three-dimensional Boussinesq equations. In the linear approximation, the model predicts the eigenmode frequencies and spatial structures, and a near-inertial wave energy signature that is characterized by an approximately time-periodic, azimuthally invariant pattern. The wave-averaged model represents the nonlinear feedback of the waves on the vortex via a wave-induced contribution to the potential vorticity that is proportional to the Laplacian of the kinetic energy density of the waves. When this is taken into account, the modal frequency is predicted to increase linearly with the energy of the initial excitation. Both linear and nonlinear predictions agree convincingly with the Boussinesq results.

Essence of Bach Flower Remedies

Flower therapy is a gift of nature to the mankind. Flowers have the power to heal the body at physical, mental and spiritual level. Humans of all ages are drawn to flowers like a moth to a flame. These flowers in vibrant colors touch our spirits. They add colors to the life, bringing abundant joy and happiness. Flowers carry specific healing energy information which when given for a particular ailment will align the vibrations within the organism bringing balance in an individual and harmony of mind, body and spirit. With each dose taken the energy signature of the flower will assist in healing the body. This new method of therapy was discovered by Edward Bach, a doctor who discovered that flowers and plants have the power to soothe and stir the soul. It was a completely different healing method which he decided to name it as Bach Flower Therapy. And so, it became a complementary and alternative system of healing the sick individual. Flower therapy is the administration of essence that is obtained from flower by boiling or by exposing them to the sunshine. These are then administered orally to calm or enhance the particular emotional state. It is simple, safe, easily administered, without any side effects, and given in a standard dose.

Estimating Building Airtightness from Data – A Case Study

The focus on energy conservation in buildings is increasing. Despite that, the yearly building renovation rate is only at around 1 %. To increase the renovation rate, new and time-efficient methods used for screening of large building portfolios’ energy saving potential are needed. In this paper, a re-engineered take on the classical energy signature method is applied to two renovated apartments in Denmark. The energy signature model relies on time-series measurements of space heat consumption, outdoor temperature, solar irradiation, and wind speed. The estimates obtained from it consist of—among other things—heat loss coefficient and wind-induced heat loss. This paper focuses on the latter. To validate the model estimate, the airtightness has been quantified by blower door-tests in both apartments: the results showed that one apartment is reasonable airtight, while the other suffers from significant air leakages. The energy signature and two other infiltration models, based on blower door test results, were compared. Good agreement between the results obtained from the data-driven energy signature and the blower door test were found. With use of a simple linear relation between the average infiltration and the blower door test result (q50), from the Danish national building code, the energy signature was found to overestimate the blower door test result (q50) by 33 % for the leaky apartment and underestimate the same air flow by 18 % for the other apartment. Both estimates are within the standard error of the infiltration model in the Danish national building code.

Assinatura Energética dos Manguezais no Domínio Costeiro Brasileiro

This research aimed to detail, from a systemic view, the mangrove ecosystem on the Brazil scale, based on the Units scale, and on the ecosystem definitions. The multiplicity of mangrove phytophysiognomies along the Brazilian coast requires specific knowledge about the conditioning agents that explain such configurations, so we sought to detail the energy signature of the Brazilian coast to better understand the dispersion, diversity and zoning of the mangrove vegetation. The database was secondary, collected in national and international articles, theses and dissertations and official websites. The results are: 1- Nine Environmental Units for the Brazilian Coastal Domain were identified, based on their general characteristics, where a previous study established eight Units; 2- The flowcharts for each Unit were prepared based on subsidiary energies, that is, average annual sunshine; annual average temperature; average annual precipitation; nutrients; average annual flow; average tidal range, thus resulting in an environmental characterization for each Unit. 3- In response to the Energy Signature of each Unit, it was possible to identify patterns of dispersion / occurrence of mangrove species with occurrence. 4- For Brazil, the most diverse genus is Rhizophora, which occurs throughout the Brazilian coast. However, only the Rhizophora mangle is present from the mouth of the Oiapoque-AM to Santa Catarina. R. racemosa and R. harrisonii are dispersed from the North Region with its southern limit in the Delta of the river Parnaíba-PI in the Northeast of Brazil.

Method for Scalable and Automatised Thermal Building Performance Documentation and Screening

In Europe, more and more data on building energy use will be collected in the future as a result of the energy performance of buildings directive (EPBD), issued by the European Union. Moreover, both at European level and globally it became evident that the real energy performance of new buildings and the existing building stock needs to be documented better. Such documentation can, for example, be done with data-driven methods based on mathematical and statistical approaches. Even though the methods to extract energy performance characteristics of buildings are numerous, they are of varying reliability and often associated with a significant amount of human labour, making them hard to apply on a large scale. A classical approach to identify certain thermal performance parameters is the energy signature method. In this study, an automatised, nonlinear and smooth approach to the well-known energy signature is proposed, to quantify key thermal building performance parameters. The research specifically aims at describing the linear and nonlinear heat usage dependency on outdoor temperature, wind and solar irradiation. To make the model scalable, we realised it so that it only needs the daily average heat use of buildings, the outdoor temperature, the wind speed and the global solar irradiation. The results of applying the proposed method on heat consumption data from 16 different and randomly selected Danish occupied houses are analysed.

Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications.