Energy Signature Research Articles

Evaluation of district heating patterns for Hungarian residential buildings: Case study of Budapest

The present study focuses on analysing the heat consumption of multifamily buildings. The study evaluates measured district heating consumption data in Budapest, Hungary. The examined 218 buildings were grouped into 11 building types based on their architectural characteristics and analysed separately. The study aims to answer the question: how the buildings' characteristics influence the energy demand of the buildings. In the buildings only the total energy consumption is measured, thus the domestic hot water energy demand was examined in the summer months, when there is no heating in the building. The domestic hot water heat loss was 37–54% of total heat consumption in the summer period. The specific total district heating consumption data [kWh/m3/day] has higher deviation in colder months. The length of heating period is 183 days in Hungary, however the average length of heating period was longer in the examined buildings and it was even longer in case of the uninsulated buildings. From the measured consumption data the energy signature curves were developed for each building type for both the insulated and uninsulated buildings separately and the results were compared and analysed in detail. The energy signature diagrams showed a significant gap between the insulated and uninsulated buildings’ total district heating consumption data in the heating period. Lines were fitted to the data and the slope of the lines fitted to uninsulated buildings’ data indicate higher decline for every building type. A separation method was also suggested to divide the data of heating and non-heating period if its term is unknown. The difference between the average slope values of the fitted lines were analysed compared to buildings’ characteristics. It aimed to examine if the consumption reduction is influenced by the buildings’ physical parameters. In conclusion, the buildings’ characteristics did not always influence the final results (e.g. domestic hot water heat loss). But uninsulated buildings tend to have longer heating periods and buildings with smaller cooling surface area per heated volume had a predominantly lower energy consumption reduction rate.

Development of a neutrino detector capable of operating in space

The $\nu$SOL experiment to operate a neutrino detector close to the Sun is building a small test detector to orbit the Earth to test the concept in space. This detector concept is to provide a new way to detect neutrinos unshielded in space. A double delayed coincidence on Gallium nuclei that have a large cross section for solar neutrino interactions emitting a conversion electron and converting the nuclei into an excited state of Germanium, which decays with a well-known energy and half-life. This unique signature permits operation of the detector volume mostly unshielded in space with a high single particle counting rate from gamma and cosmic ray events. The test detector concept which has been studied in the lab and is planned for a year of operations orbiting Earth which is scheduled for launch in late 2024. It will be surrounded by an active veto and shielding will be operated in a polar orbit around the Earth to validate the detector concept and study detailed background spectrums that can fake the double timing and energy signature from random galactic cosmic or gamma rays. The success of this new technology development will permit the design of a larger spacecraft with a mission to fly close to the Sun and is of importance to the primary science mission of the Heliophysics division of NASA Space Science Mission Directorate, which is to better understand the Sun by measuring details of our Sun's fusion core.

Probing light exotics from a hidden sector at c−τ factories with polarized electron beams

Future $c\text{\ensuremath{-}}\ensuremath{\tau}$ factories are natural places to study extensions of the Standard Model (SM) of particle physics with new long-lived feebly interacting particles light enough to be produced in electron-positron collisions. We investigate prospects of these machines in exploring such extensions emphasizing the role of polarized beams in getting rid of the SM irreducible background for the missing energy signature. We illustrate this on the example of the $c\text{\ensuremath{-}}\ensuremath{\tau}$ project in Novosibirsk, where the electron beam is designed to be polarized to achieve much higher sensitivity to hadronic resonances and $\ensuremath{\tau}$ leptons. We investigate models with hidden photons, with millicharged particles (fermions and scalars), with ${Z}^{\ensuremath{'}}$ bosons and with axionlike particles. We find that the electron beam polarization of 80% significantly improves the chances to observe the signal, especially with large statistics. We outline the regions of the model parameter space which can be reached at this factory in one year and in ten years of operation according with the scientific schedule of tuning the energy of colliding beams.

A Study of the Effects of Appliance Energy Signatures on Different Neural Network Types in Nonintrusive Load Monitoring

A Study of the Effects of Appliance Energy Signatures on Different Neural Network Types in Nonintrusive Load Monitoring

Probing hidden spin-2 mediator of dark matter with NA64e , LDMX, NA64μ , and M3

The connection between Standard Model (SM) particles and dark matter (DM) can be introduced via a hidden spin-2 massive mediator. In the present paper we consider the simplified benchmark link between the charged lepton sector of SM and DM particles, which are considered to be hidden Dirac fermions from the dark sector. The regarding couplings are established through the dimension-five operators involving spin-2 mediator field and the energy-momentum tensors of both SM and DM sectors. We study in detail the implication of this scenario for the lepton fixed-target facilities, such as $\mathrm{NA}64e$, LDMX, $\mathrm{NA}64\ensuremath{\mu}$, and ${\mathrm{M}}^{3}$; in particular, for the specific experiment we discuss in detail, the missing energy signatures of spin-2 boson production followed by its invisible decay into stable DM pairs. Moreover, we derive the expected reaches of these experiments for the projected statistics of the leptons accumulated on the target. We also discuss the implication of both nuclear and atomic form-factor parametrizations for the differential spectra of hidden spin-2 boson emission, the total cross section of its production, and the experimental reach of the fixed-fixed-target facilities for probing hidden spin-2 DM mediator. We briefly discuss the DM relic abundance parameter space that can be probed by these experiments.

Research on the energy signature of a residential building in an arid climate of Errachidia city (South Eastern Morocco)

Energy efficiency in the building sector has attracted a great deal of interest in recent years; because it is seen as a potential solution to minimize the high energy consumption caused by the acceleration of the urbanization process. Several methodologies have been developed to assess the energy performance of buildings, including the energy signature, which is an empirical tool used to represent the real energy performance of buildings. Synthetic data of energy consumption of buildings are generated by ECOTECT simulation program, which is a class of energy models that uses characteristic data (dimensional, physical and location) of the building to predict energy consumption. The primary goal of this research is to examine how solar rays and occupants affect a residential building's energy signature in Errachidia, a city in the southeast of Morocco characterized by its arid climate. The results show that when solar radiation was taken into account, the outcomes of the building's intrinsic characteristics (a and b) enhanced the signature. The distinct response in energy use serves as justification. In fact, the solar radiation provides a significant improvement with an important R2 accuracy of about 0.999. Moreover, the introduction of the occupancy factor has a significant impact on energy savings and temperature fluctuations in energy consumption. The importance of parameter b related to the fixed loads of the building is more influenced by the occupancy factor. This opens up a new possibility for energy-saving studies in the case of an arid climate.

Phenomenology at the large hadron collider with deep learning: the case of vector-like quarks decaying to light jets

In this work, we continue our exploration of TeV-scale vector-like fermion signatures inspired by a Grand Unification scenario based on the trinification gauge group. A particular focus is given to pair-production topologies of vector-like quarks (VLQs) at the LHC, in a multi-jet plus a charged lepton and a missing energy signature. We employ Deep Learning methods and techniques based in evolutive algorithms that optimize hyper-parameters in the neural network construction, whose objective is to maximise the Asimov estimate for distinct VLQ masses. In this article, we consider the implications of an innovative approach by simultaneously combining detector images (also known as jet images) and tabular data containing kinematic information from the final states. With this technique we are able to exclude VLQs, that are specific for the considered model, up to a mass of 800 GeV in both the high-luminosity the Run-III phases of the LHC programme.

A study on data-driven hybrid heating load prediction methods in low-temperature district heating: An example for nursing homes in Nordic countries

In the face of green energy initiatives and progressively increasing shares of more energy-efficient buildings, there is a pressing need to transform district heating towards low-temperature district heating. The substantially lowered supply temperature of low-temperature district heating broadens the opportunities and challenges to integrate distributed renewable energy, which requires enhancement on intelligent heating load prediction. Meanwhile, to fulfill the temperature requirements for domestic hot water and space heating, separate energy conversion units on user-side, such as building-sized boosting heat pumps shall be implemented to upgrade the temperature level of the low-temperature district heating network. This study conducted hybrid heating load prediction methods with long-term and short-term prediction, and the main work consisted of four steps: (1) acquisition and processing of district heating data of 20 district heating supplied nursing homes in the Nordic climate (2016–2019); (2) long-term district heating load prediction through linear regression, energy signature curve in hourly resolution, providing an overall view and boundary conditions for the unit sizing; (3) short-term district heating load prediction through two Artificial Neural Network models, f72 and g120, with different prediction input parameters; (4) evaluation of the predicted load profiles based on the measured data. Although the three prediction models met the quality criteria, it was found that including the historical hourly heating loads as the input to the forecasting model enhanced the prediction quality, especially for the peak load and low-mild heating season. Furthermore, a possible application of the heating load profiles was proposed by integrating two building-sized heat pumps in low-temperature district heating, which may be a promising heat supply method in low-temperature district heating.

Kinetic mixing, dark Higgs triplets, and MW

The kinetic mixing (KM) portal is a popular mechanism that allows light dark matter (DM) in the mass range below $\ensuremath{\sim}1\text{ }\text{ }\mathrm{GeV}$ to achieve the observed relic density by thermal means and can be effectively described by only a few parameters, e.g., $\ensuremath{\epsilon}$, the strength of this KM. In the simplest setup, the Standard Model (SM) $U(1{)}_{Y}$ hypercharge gauge boson and a $U(1{)}_{D}$ dark photon (DP), which only couples to fields in the dark sector, experience KM via loops of portal matter (PM) fields which have both SM and dark charges, thus generating a small coupling between us and dark matter (DM). However, if one wishes to understand the underlying physics behind this idea in a deeper fashion we need to take a step upward to a more UV-complete picture. Meanwhile, CDFII has measured a value for the $W$-boson mass which lies significantly above SM expectations. In this paper, we speculate that this shift in the $W$'s mass may be related to the $\ensuremath{\sim}1\text{ }\text{ }\mathrm{GeV}$ mass of the DP within a framework of scalar PM that leads to phenomenologically interesting values of $\ensuremath{\epsilon}$ via non-Abelian KM due to the existence of an $SU(2{)}_{L}$, $Y=0$, complex Higgs triplet which carries a nonzero value of the $U(1{)}_{D}$ dark charge. Possible gauge boson plus missing energy signatures of this scenario that can appear at the LHC and elsewhere are examined. Indeed, with modest assumptions, all of the new scalar PM states are predicted to have masses below roughly $\ensuremath{\simeq}630\text{ }\text{ }\mathrm{GeV}$ and so should be at least kinematically accessible. The HL-LHC will very likely to be able to explore all of this model's allowed parameter space.

Large Scale Energy Signature Analysis: Tools for Utility Managers and Planners

Building energy signature analysis is a well-established tool for understanding the temperature sensitivity of building energy consumption and measuring energy savings. This tool has been used to measure energy savings of residential, commercial, and even industrial buildings. The public availability of electricity loads (i.e., hourly electricity demand (MW)) from entire Balancing Authorities (BAs) provide an interesting opportunity to apply this approach to a large aggregate load. In this paper, we explore that opportunity for BAs and show that the correlations for large geographical areas are surprisingly coherent when the change-point linear regression analysis is used with the daily interval data of electricity demand and outside air temperature. The change-point linear regression models of all the BAs, except WAUW and OVEC, show R2 of 0.70 or more and CV-RMSE of 10.0% or less. We also suggest an analysis method that allows for meaningful comparisons between BAs and to assess changes in time for a given BA which could be used to interpret changes in load patterns year-to-year, accounting for changes in weather. This approach can be used to verify the impact of energy efficiency programs on a building component/system-wide basis. This study shows the annual electricity demand reductions for SCL and IPCO are 136,655 MWh (1.5%) and 182,053 MWh (1.1%), respectively.

Examination of the multitude of signals from the phase transition of a neutron star to a quark star

Aim: To study the multimessenger nature of the signal that can result from the phase transition of a neutron star to a quark star and their corresponding astrophysical observations. Methods: The phase transition process is initiated by the abrupt pressure and density changes at the star center, giving rise to a shock which deconfines matter followed by a weak front converting excess down to strange quarks to attain absolute stability. This process's effects are investigated by understanding how the energy escapes from the star in the form of neutrino-antineutrino annihilation. For such annihilation process, the corresponding energy deposition rate is calculated. Structural changes due to the energy loss have been investigated in the likes of misalignment angle evolution of the star and its astrophysical observation through gravitational waves. Results: The energy and time signature for the neutrino-antineutrino annihilation is compared with the observed isotropic energy for a short gamma-ray burst. The misalignment angle evolves to align the star's tilt axis, which can lead to the sudden increase or decrease of radio intensity from the pulsar. The corresponding gravitational wave emission, both continuous and burst, all lead towards multimessenger signals coming from the phase transition.

Detecting heavy Higgs bosons from natural SUSY at a 100 TeV hadron collider

Supersymmetric models with radiatively-driven naturalness (RNS) enjoy low electroweak fine-tuning whilst respecting LHC search limits on gluinos and top squarks and allowing for $m_h\simeq 125$ GeV. While the heavier Higgs bosons $H,\ A$ may have TeV-scale masses, the SUSY conserving $\mu$ parameter must lie in the few hundred GeV range. Thus, in natural SUSY models there should occur large heavy Higgs boson branching fractions to electroweakinos, with Higgs boson decays to higgsino plus gaugino dominating when they are kinematically accessible. These SUSY decays can open up new avenues for discovery. We investigate the prospects of discovering heavy neutral Higgs bosons $H$ and $A$ decaying into light plus heavy chargino pairs which can yield a four isolated lepton plus missing transverse energy signature at the LHC and at a future 100~TeV $pp$ collider. We find that discovery of heavy Higgs decay to electroweakinos via its $4\ell$ decay mode is very difficult at HL-LHC. For FCC-hh or SPPC, we study the $H,\ A \to $ SUSY reaction along with dominant physics backgrounds from the Standard Model and devise suitable selection requirements to extract a clean signal for FCC-hh or SPPC with $\sqrt{s}=100$ TeV, assuming an integrated luminosity of 15 $ab^{-1}$. We find that while a conventional cut-and-count analysis yields a signal statistical significance greater than $5\sigma$ for $m_{A,H}\sim 1.1-1.65$ TeV, a boosted-decision-tree analysis allows for heavy Higgs signal discovery at FCC-hh or SPPC for $m_{A,H}\sim 1-2$ TeV.

Bayesian Workflow and Hidden Markov Energy-Signature Model for Measurement and Verification

A Bayesian data analysis workflow offers great advantages to the process of measurement and verification, including the estimation of savings uncertainty regardless of the chosen numerical model. However, it is still rarely used in practice, perhaps because practitioners are less familiar with the required tools. The present work documents a Bayesian methodology for the assessment of energy savings at the scale of a whole facility, following an energy-conservation measure. The first model, an energy signature commonly used in practice, demonstrates the steps of the Bayesian workflow and illustrates its advantages. The posterior distributions obtained by training this first model are used as prior distributions for a second, more complex model. This so-called “hidden Markov energy signature” model combines the energy signature with a hidden Markov model at an hourly resolution, and allows detection of occupancy. It has a large number of parameters and would likely not be identifiable without the Bayesian workflow. The results illustrate the advantages of the Bayesian methodology for measurement and verification: a probabilistic description of all variables, including predictions of energy use and savings; the applicability to any model structure; the ability to include prior knowledge to facilitate training complex models. Savings are estimated by the new hidden Markov energy-signature model with a much lower uncertainty than with a lower-resolution model. The highlights of the paper are twofold: it serves as a tutorial on Bayesian inference for measurement and verification; it also proposes a new flexible model structure for hourly prediction of energy use and occupancy detection.

The influence of weather on heat demand profiles in UK social housing tower blocks

Prediction of heat demand is of distinct importance for policy planning in social housing, where residents are in higher danger of falling into fuel poverty. Understanding the behavioural response of fuel vulnerable households against weather allows generating accurate baseline energy models and estimations of energy savings. This paper evaluates weekly heat demand profiles of 462 social housing dwellings in five tower blocks in the South of the UK, monitored over two years. Linear and segmented regressions are fitted through the ‘segmented’ package in R Studio to explore the relationship between heat demand (including Domestic Hot Water and space heating) and Outdoor Temperature, generating energy signature models for each flat. Three distinct heat demand profiles are found: (i) households that do not use space heating (11%), (ii) irregular consumers, where the transition towards the heating season is not identifiable (33%), and (iii) households with marked seasonal thresholds (56%). Consumption trends as well as the effect of extremely weather events such as the 2018 storm ‘The Beast from the East’ on the heat demand are evaluated. Low consuming households show little to no variation in their demand patterns during extreme weather events, whereas higher consumers seem to reach a plateau in their demand even at extremely low temperatures. The variability of heat demand in dwellings which have identical physical properties, and are exposed to the same weather conditions, is attributed to occupant behaviour. This study highlights the heterogeneity of heat demand in social housing and the need to move away from national averages.

Holistic Operational Signatures for an energy-efficient district heating substation in buildings

District heating (DH) is recognized as a sustainable energy infrastructure in cities and buildings. DH substation operation is essential to bridging the gap between plants and thermal clients in achieving the intended DH values and visions. Therefore, in this study, an operation analysis method was developed for building-side DH substations. Specifically, Holistic Operational Signature (HOS) was suggested with multiple signature elements (x-OS) and representations to consider comprehensive operational correlations. A HOS-based analytics is proposed to provide more insights and sound reasoning about the DH substation design, operation, control, and heating efficiency potentials, compared with the existing Energy Signature. In a case study for winter, the proposed HOS method was applied to a target DH substation serving actual residential buildings using the real operational datasets. The six HOSs (HOSs 1–6) were derived with their six elements to identify the representative heating consumption levels and patterns, partial heating load ratios, and operation and control efficiency. Excessive water flow rates were identified based on the partial heating load elements (less than 50% in most cases) of all signatures. Inefficient signatures (HOSs 1–4) that do not follow the conventional control principle according to outdoor air temperatures were captured, especially for high heating demand patterns, which accounted for 72.5% of the total in winter. According to the signatures, the demand-based control can decrease the heating supply temperature setpoint by about 1.4 °C and 0.6 °C, respectively for HOS2 and the winter. It is recommended that the valve operation should be improved to lower the hunting levels (within ±1.5 °C) of heating water temperature control, especially in the afternoon with low heating demands.

Investigating Energy Use in a City District in Nordic Climate Using Energy Signature

This paper focuses on multi-family buildings in a Swedish city district, erected between 1965 and 1973, which are now in need of renovation. For the two types of multi-family buildings in the district, tower buildings and low-rise buildings, dynamic energy use is predicted by using an energy signature method. The energy signature is then used to calculate the primary energy use number of the building stock, according to calculations methods dictated by Swedish building regulations. These regulations are also used to assess which multi-family buildings are in need of renovation, based on the buildings’ primary energy use. For buildings that need energy renovations, it is simulated so that the energy use of each multi-family building complies with these same building regulations. The proposed methodology for simulating energy renovation also determines new energy signature parameters, related to building heat loss coefficient, balance temperature and domestic hot water usage. The effects of simulated renovation are displayed in a duration diagram, revealing how a large-scale renovation affects the district’s heat load in different annual periods, which affects the local district heating system. Sensitivity analysis is also performed before and after simulated energy renovation.

Community composition and spatial energetics of mesopelagic fishes and squids in the eastern Bering Sea as influenced by habitat variables

Micronekton of the mesopelagic zone (200 m–1000 m depth) play a central role in most aspects of global marine ecology linking epipelagic and deep scattering layers as planktivores, carnivores and principal prey to both shallow diving and deep living apex predators. Yet, despite this critical role and conspicuous presence, little is known regarding most aspects of mesopelagic community ecology, including habitat associations at sub-basin spatial scales. Here we address several aspects of these data deficiencies in the eastern Bering Sea mesopelagic – a highly productive subarctic marine ecosystem home to multiple protected predator species and large commercial fisheries. Through 41 midwater trawls conducted at 250 m, 500 m and 1000 m depths during May of 1999 and 2000, and a series of generalized linear models and cluster analyses, we describe the influence of remotely sensed oceanographic habitat variables on the diel distribution, relative abundance and community composition of 30 species of mesopelagic fish and squid in 3 contrasting study areas. We then project the total energy available to predators at depth and across the eastern Bering Sea Basin based on the identified habitat constraints. Fishes from the families Bathylagidae and Myctophidae, and squids from the Gonatidae dominated the 72 species catch biomass (n = 225,000, 2,100 kg). The six most abundant species were equally divided between each of the three dominant families and are also the most highly represented mesopelagic species in eastern Bering Sea pelagic predator diets. Their association with surface habitat variables describe a complex and multifaceted relationship between physical/biological predictors and the total mesopelagic energy field during the spring bloom period in this ecosystem. Leuroglossus schmidti, Bathylagus pacificus, Stenobrachius leucopsaurus, S. nannochir, Gonatopsis borealis and Eogonatus tinro contrast in the suite of physical and biological variables that they respond to, and also in the character of their response. Although L. schmidti is of moderate caloric value, it expressed the greatest overall energy signature at all depths due to its overwhelming biomass, and response to the presence of specific, variable habitat conditions across extensive portions of the study area. Interannual variability in habitat variables forecasted a pronounced redistribution of dominant species and thereby total mesopelagic energy content – providing a potential mechanism for shifts in central-place predator habitat use, energy expenditure, and diet. The known relevance of mesopelagic species as prey and the energetic potential for apex predators foraging in the Basin illustrates a cautionary narrative regarding unregulated harvest of biota from this poorly understood and critically important oceanic zone.

Uncovering input-sensitive energy bottlenecks in oversubscribed GPU workloads

Graph algorithms are at the core of data-intensive applications in many computational domains. Although accelerator-based implementations have the potential for delivering improved energy efficiency on today’s large-scale graph analytics, there are many challenges to reaching the desired goals. This paper examines the issues of GPU oversubscription and input sensitivity in energy efficient graph processing. We introduce the notion of energy signatures, a collection of runtime events that can determine fluctuations in power consumption as a function of input graph characteristics. The study shows that variations in volume, density, diameter and dispersion in graphs can alter the runtime behavior of GPU applications and these changes in program behavior in turn impact the energy efficiency of the application. Based on the insight gained from our study, we develop three input-aware optimization techniques to mitigate energy bottlenecks. On experiments conducted on a Unified Memory supported GPU, the optimizations yield a 11.97% energy savings on average on Breadth-First Search and Single-Source Shortest Path algorithms.

Spatiotemporal upscaling errors of building stock clustering for energy demand simulation

Energy demand of buildings forms a key component of energy system analyses. To integrate building energy demand into energy system models, detailed simulation of every single building is impractical and often computationally infeasible. Simulations are therefore typically performed on a sample of buildings for bottom-up analysis. For a simplified representation of the building stock, grouping and clustering are widely applied. However, a spatiotemporal quantification of the resulting upscaling errors is however lacking. Here, a grouping and clustering approach is performed for all Swiss buildings for bottom-up energy demand analysis. The spatiotemporal simulation error is quantified at the national, regional and neighbourhood (300×300 m) scales. Whereas national energy demands are well represented, the error induced by the grouping and clustering is significant at the neighbourhood scale: simulated demand is easily over or underestimated by double-digit percentages. Obtained regional full load hours and daily load factor values by a grouping and clustering approach are comparable to a full simulation of all buildings. However, careful consideration must be given to the used climate data if relying on clustering approaches. Our analysis reveals the challenge of representing peak demands well by aggregation of building demand profiles, which is exacerbated by grouping and clustering. Bottom-up studies thus need to consider errors arising from the grouping and clustering approach and distinguish between errors resulting from the upscaling process and other sources of uncertainty such as data inputs or modelling concepts. Finally, calculated energy signatures are shown to perform well as a fast and viable alternative to grouping and clustering.

Anomalous couplings in Zγ events at NNLO+PS and improving [formula omitted] backgrounds in dark-matter searches

The measurement of the triple gauge couplings (TGCs) is a central part of diboson studies at the LHC. In this letter we consider the Zγ process and include anomalous TGCs (aTGCs) in the event generation at next-to-next-to-leading order QCD accuracy (NNLO+PS) within the MiNNLOPS framework. While our implementation is fully general and applies to both Z→ℓ+ℓ− and Z→νν¯ decays, we focus here on the νν¯γ final state. After validation of our simulation of νν¯γ events, for which NNLO+PS accuracy is achieved for the first time, the effects of aTGCs on various distributions are studied. Moreover, we show the relevance of NNLO+PS accuracy for the νν¯γ background to photon plus missing energy signatures in dark-matter searches, and we compare MiNNLOPS predictions for νν¯γ production to recent 13 TeV data.