Peder Bacher

• Technical University of Denmark

This work presents a scalable Bayesian modeling framework for evaluating building energy performance using smart-meter data from 2,788 Danish single-family homes. The framework leverages Bayesian statistical inference integrated with Energy Signature (ES) models to characterize thermal performance in buildings. This approach quantifies key paramete...

Ultra-low temperature (ULT) freezers are used to store perishable biological contents and are among the most energy-intensive equipment in laboratory buildings, biobanks, and similar settings. To ensure reliable and efficient operation, it is essential to implement data-driven fault detection and diagnostic algorithms, along with energy optimizatio...

Within the field of statistical modelling and data-driven characterisation of buildings’ energy performance, the focus is typically on parameter estimation of the building envelope and the energy systems. Less focus has been put on the stochastic human effect on energy consumption. We propose a new method for estimating the thermal building propert...

Ultra-low temperature (ULT) freezers store perishable bio-contents and have high energy consumption, which highlight a demand for reliable methods for intelligent surveillance and smart energy management. This study introduces a novel grey-box modelling approach based on stochastic differential equations to describe the heat dynamics of the ULT fre...

The data presented here were collected independently for 6 real buildings by researchers of different institutions and gathered in the context of the IEA EBC Annex 81 Data-driven Smart Buildings, as a joint effort to compile a diverse range of datasets suitable for advanced control applications of indoor climate and energy use in buildings. The dat...

The paper presents the learnings from designing and running a model predictive control (MPC) of the heating system in a school building. Several real-life applications of MPC controlled heating have been presented in the literature. Most of them work by controlling the room temperature usingn a heating system and thus need a reference measured temp...

Uncertainty is one of the core challenges posed by renewable energy integration in power systems, especially for solar photovoltaic (PV), given its dependence on meteorological phenomena. This has motivated the development of numerous forecasting tools, recently focused on physics informed machine learning (ML). Virtually, every paper claims to pro...

The intermittency of renewable energy sources, such as wind and solar, means that they require reliable and accurate forecasts to integrate properly into energy systems. This review introduces and examines a selection of state‐of‐the‐art methods that are applied for multivariate forecasting of wind and solar power production. Methods such as condit...

Increasingly advanced stochastic energy management systems are employed to facilitate the integration of wind and solar PV in worldwide power grids. In this context, forecasting is a key tool limiting the success of said control actions. This paper explores the suitability of stacked machine learning based models to predict wind and solar power ava...

Aiming to enable robust large-scale fault diagnostics and optimized control for supermarket refrigeration systems, a data-driven grey box model for an evaporator and its surrounding cooling cabinet (or room) is presented. It is a non-linear model with two states: the cabinet temperature and the refrigerant mass in the evaporator. To demonstrate its...

This paper proposes non-linear autoregressive models with exogenous inputs to model the air temperature in each room of a Danish school building connected to the local district heating network. To obtain satisfactory models, the authors find it necessary to estimate the solar radiation effect as a function of the time of the day using a B-spline ba...

In many applications, e.g. fault diagnostics and optimized control of supermarket refrigeration systems, it is important to determine the heat demand of the cabinets. This can easily be achieved by measuring the mass flow through each cabinet, however, that is expensive and not feasible in large-scale deployments. Therefore it is important to be ab...

Machine Learning (ML)-based methods have been identified as capable of providing up to one day ahead Photovoltaic (PV) power forecasts. In this research, we introduce a generic physical model of a PV system into ML predictors to forecast from one to three days ahead. The only requirement is a basic dataset including power, wind speed and air temper...

Home Energy Management Systems (HEMSs) are expected to become an inevitable part of the future smart grid technologies. To work effectively, HEMSs require reliable and accurate load forecasts. In this paper, two new modelling methods are presented. They are both suited for producing multivariate probabilistic forecasts, which consider the temporal...

Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the R package onlineforecast that provides a generalized setup of data and models for online foreca...

This paper introduces a non-linear grey-box (GB) model based on stochastic differential equations that describes the heat dynamics of a school building in Denmark, equipped with a water-based heating system. The building is connected to a local district heating network through a heat exchanger. The heat is delivered to the rooms mainly through radi...

Smart and flexible operation of components in district heating systems can play a crucial role in integrating larger shares of renewable energy sources in energy systems. Buildings are one of the crucial components that will enable flexibility in the district heating by using intelligent operation. Recent work suggests that such improved operation...

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 signatu...

The future energy system is weather-driven. To take full and effective advantage of the renewable energy production, we need to make the demand flexible, such that it better coincides with the weather-driven energy production. We argue that this disruption of the energy system implies a need for new planning and control methodologies for the energy...

With the aim of enabling effective flexible electricity demand, a machine learning algorithm is developed to forecast the CO2 emission intensities in European electrical power grids distinguishing between average and marginal emissions. The analysis focuses on Danish bidding zone DK2 and was done on a data set comprised of a large number (473) of e...

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 document...

An optimized heat pump control for building heating was developed for minimizing CO2 emissions from related electrical power generation. The control is using weather and CO2 emission forecasts as inputs to a Model Predictive Control (MPC)—a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimized...

An optimized heat pump control for building heating was developed for minimizing CO2 emissions from related electrical power generation. The control is using weather and CO2 emission forecasts as input to a Model Predictive Control (MPC) - a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimize...

Electric water heater (EWH) is widely used to provide reliable and long-lasting domestic hot water to occupants in residential buildings. EWH has been widely recognized as an important source of building energy flexibility, which could benefit both the building occupants and the power system operators through various demand response (DR) programs....

A machine learning algorithm is developed to forecast the CO2 emission intensities in electrical power grids in the Danish bidding zone DK2, distinguishing between average and marginal emissions. The analysis was done on data set comprised of a large number (473) of explanatory variables such as power production, demand, import, weather conditions...

Modelling the effects of solar irradiation plays an important role in various applications. This paper describes a semi-parametric (combined grey-box and spline-based), data-driven technique that can be used to model systems in which the solar gain depends on the sun position. The solar gain factor is introduced, i.e. the absorbed fraction of measu...

In-situ non-intrusive data to identify the heat loss coefficient (HTC) of a building is promising but possibly not sufficiently informative, let alone for disaggregating losses through separate parts of the envelope. This paper presents a numerical procedure to assess the ability of models to accurately identify overall and disaggregated heat trans...

Assessing the energy-saving potential in a building stock requires accurate prediction of the energy use in buildings, as well as estimating effects of imposing energy-conservation measures. Bottom-up building physics-based building stock energy models are widely used for this purpose. However, deficient data (e.g. data related to the use of the bu...

Real energy performance of new and retrofitted buildings often consistently differs from expectations. While occupants might complain about poor indoor climate, the energy use in such buildings is often higher than expected, leading to the well-known phenomenon called "Energy Performance Gap". In the past years, monitoring of buildings, both in ter...

This paper explores the concept of characterizing the as-built Heat Loss Coefficient (HLC) of buildings based on-board monitoring (OBM), via energy consumption and temperature sensors, and time series analysis. It is examined (1) how the coefficients of different Auto-Regressive with eXogenous inputs (ARX) models can be interpreted and (2) whether...

Real energy performance of new and retrofitted buildings often consistently differs from expectations. While occupants might complain about poor indoor climate, the energy use in such buildings is often higher than expected, leading to the well-known phenomenon called “Energy Performance Gap”. In the past years, monitoring of buildings, both in ter...

The energy-saving potential in buildings (e.g. buildings proposed for an energy upgrade in an energy policy context) is often overestimated because implicit factors, such as rebound effects, are ignored. In order to get an accurate estimate of the realisable energy-saving potential in a building stock, these factors, as well as how they differ amon...

Variability of large-scale wind and solar energies as power sources creates a disadvantage and a challenge for frequency control in distribution networks. In this work, an Advanced Energy Management System (AEMS) for a hybrid generation system is proposed. The AEMS is based on tracking the load curve or final user's required power to be supplied by...

This paper complements existing Smart City taxonomies with a case study of concrete cross-boundary collaboration between actors from diverse disciplines and institutions. The paper explores technical, social and organizational aspects of indoor climate in public buildings in Copenhagen, and outlines a digital platform (skoleklima.dk/climify.org) fo...

Smart gadgets overflowed the market within the last years. Connected cars, automated houses, smart wearables are changing our everyday life. Smart gadgets help us solving many complex tasks in an optimal way. In homes for instance, we can let them run our heating, ventilation and air conditioning systems (HVAC). Within the Smart Cities Accelerator...

In some district heating systems, greenhouses represent a significant share of the total load, and can lead to operational challenges. Short term load forecast of such consumers has a strong potential to contribute to the improvement of the overall system efficiency. This work investigates the performance of recursive least squares for predicting t...

The lighter weight, improved thermal properties and better acoustic insulation of hollow-core concrete blocks are few of the characteristics that one encounters when comparing them to traditional Maltese globigerina limestone solid blocks. As a result, hollow concrete blocks have recently been in greater demand. However, their transmittance, or U-v...

Several studies have shown that the actual thermal performance of buildings after construction may deviate significantly from its performance anticipated at design stage. As a result, there is growing interest in on site testing as a means to assess real performance. The IEA EBC Annex 58-project ‘Reliable Building Energy Performance Characterisatio...

The challenges to optimally utilize weather dependent renewable energy sources call for powerful tools for forecasting. This paper presents a non-parametric machine learning approach used for multi-site prediction of solar power generation on a forecast horizon of one to six hours. Historical power generation and relevant meteorological variables r...

Hybrid systems are implemented to improve the efficiency of individual generation technologies by complementing each other. Intermittence is a challenge to overcome especially for renewable energy sources for electric generation, as in the case of wind power. This paper proposes a hybrid system as an approach for reducing and overcoming the volatil...

In this paper a method for separating spikes from a noisy data series, where the data change and evolve over time, is presented. The method is applied on measurements of the total heat load for a single family house. It relies on the fact that the domestic hot water heating is a process generating short-lived spikes in the time series, while the sp...

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Several studies have shown that actual thermal performance of buildings after construction may deviate significantly from that anticipated at design stage. As a result, there is growing interest in full scale testing of components and whole buildings. The IEA EBC Annex 58-project ‘Reliable Building Energy Performance Characterisation Based on Full...

This paper presents a study of models for forecasting the electrical load for supermarket refrigeration. The data used for building the models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village in Denmark. Every hour the hourly electrical load for refrig...

The present paper describes modelling of the thermal dynamics of a real wall tested in dynamic outdoor weather conditions, to identify all the parameters needed for its characterisation. Specifically, the U value, absorptance and effective heat capacity are estimated for the wall using grey-box modelling based on statistical methods and known physi...

This paper presents a study of models for forecasting the load for supermarket refrigeration. The data used for building the forecasting models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village in Denmark. Every hour the hourly load for refrigeration fo...

This paper presents a method for forecasting the load for heating in a single-family house. Both space and hot tap water heating are forecasted. The forecasting model is built using data from sixteen houses in Sønderborg, Denmark, combined with local climate measurements and weather forecasts. Every hour the hourly heat load for each house the foll...

This paper presents the grey-box modeling of a vapor-compression refrigeration system for residential applications based on maximum likelihood estimation of parameters in stochastic differential equations. Models obtained are useful in the view of controlling refrigerators as flexible consumption units, which operation can be shifted within tempera...

This paper presents a method for correction and alignment of global radiation observations based on information obtained from calculated global radiation, in the present study one-hour forecast of global radiation from a numerical weather prediction (NWP) model is used. Systematical errors detected in the observations are corrected. These are error...

In this work the heat dynamics of a storage tank were modelled on the basis of data and maximum likelihood methods. The resulting grey-box model was used for Economic Model Predictive Control (MPC) of the energy in the tank. The control objective was to balance the energy from a solar collector and the heat consumption in a residential house. The s...

This paper deals with grey-box modelling of the energy transfer of a double skin Building Integrated Photovoltaic (BIPV) system. Grey-box models are based on a combination of prior physical knowledge and statistics, which enable identification of the unknown parameters in the system and accurate prediction of the most influential variables. The exp...

A comprehensive improvement of the mathematical model for the so called transfer function method is presented in this study. This improved transfer function method can estimate the traditional solar collector parameters such as zero loss coefficient and heat loss coefficient. Two new collector parameters t and mfCf are obtained. t is a time scale p...

The present paper suggests a procedure for identification of suitable models for the heat dynamics of a building. Such a procedure for model identification is essential for better usage of readings from smart meters, which is expected to be installed in almost all buildings in the coming years. The models can be used for different purposes, e.g. co...

Modeling of heat dynamics of houses have been reported successful using linear dynamical models. The room they leave for improvement is – because of physical relations – believed to be partly caused by non-linear relations. As model complexity increases, detailed measurements and highly modular experiments are gaining importance in estimation of mo...

The need for fast and accurate performance testing of solar collectors is increasing. This paper describes a new technique for performance testing which is based on non-linear continuous time models of the heat dynamics of the collector. It is shown that all important performance parameters can be accurately estimated with measurements from a singl...

This paper describes a new approach to online forecasting of power output from solar thermal collectors. The method is suited for online forecasting in many applications and in this paper it is applied to predict hourly values of power from a standard single glazed large area flat plate collector. The method is applied for horizons of up to 42 hour...

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in...

From being a subject mainly considered by environmental idealists, impacts of the use of fossil fuels have become a political issue of highest priority in recent years. As developing of the technology and migration to cheap sustainable energy sources is a long and expensive process, and energy savings are becoming more and more important. Energy op...

A first research version is now in operation of a software package for multiple linear regression (MLR) modeling and analysis of solar collectors according to ideas originating all the way from Walletun et. al. (1986), Perers, (1987 and 1993). The tool has been implemented in the free and open source program R http://www.r-project.org/. Application...