A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models

Building energy performance characterisation based on dynamic analysis and co-heating test

A demonstration zero-carbon neighborhood is being raised in the city of Kortrijk, Belgium in the framework of the ECO-Life project within the CONCERTO initiative. A holistic approach is applied to achieve the zero-carbon targets, considering all aspects that are relevant for energy supply. Accordingly, alongside the integration of renewable energy sources in the community, a low-temperature district heating system is being implemented to cover the heat demand. In this context, full scale testing of building thermal performances, by use of a co-heating test and flux measurements, can be useful to analyze the thermal performance of the building envelope in situ. For that reason, as part of a more general study regarding low-energy building, co-heating test, blower-door test and flux measurements in several apartments were executed. Therefore, the paper focuses on characterization of the thermal dynamic behavior of an apartment, as a first approximation of data analysis of a monitoring system involving whole buildings. In addition, in the present study, the capability of linear regression techniques to characterize the thermal behavior of a newly built low-energy apartment in Belgium is investigated. The strengths and weaknesses of different models are identified. The limitation and possibilities of regression models are evaluated in the face of their applicability as a simplified building equation model. The identified model structure is going to be used within a complex simulation model of an entire district heating system with around 200 dwelling. Finally, the potential of this kind of regression models to be used as part of the operational control scheme of a district heating system is presented

Heuristic optimization of clusters of heat pumps: A simulation and case study of residential frequency reserve

The technological challenges of adapting energy systems to the addition of more renewables are intricately interrelated with the ways in which markets incentivize their development and deployment. Households with own onsite distributed generation augmented by electrical and thermal storage capacities (prosumers), can adjust energy use based on the current needs of the electricity grid. Heat pumps, as an established technology for enhancing energy efficiency, are increasingly seen as having potential for shifting electricity use and contributing to Demand Response (DR). Using a model developed and validated with monitoring data of a household in a plus-energy neighborhood in southern Germany, the technical and financial viability of utilizing household heat pumps to provide power in the market for Frequency Restoration Reserve (FRR) are studied. The research aims to evaluate the flexible electrical load offered by a cluster of buildings whose heat pumps are activated depending on selected rule-based participation strategies. Given the prevailing prices for FRR in Germany, the modelled cluster was unable to reduce overall electricity costs and thus was unable to show that DR participation as a cluster with the heat pumps is financially viable. Five strategies that differed in the respective contractual requirements that would need to be agreed upon between the cluster manager and the aggregator were studied. The relatively high degree of flexibility necessary for the heat pumps to participate in FRR activations could be provided to varying extents in all strategies, but the minimum running time of the heat pumps turned out to be the primary limiting physical (and financial) factor. The frequency, price and duration of the activation calls from the FRR are also vital to compensate the increase of the heat pumps’ energy use. With respect to thermal comfort and self-sufficiency constraints, the buildings were only able to accept up to 34% of the activation calls while remaining within set comfort parameters. This, however, also depends on the characteristics of the buildings. Finally, a sensitivity analysis showed that if the FRR market changed and the energy prices were more advantageous, the proposed approaches could become financially viable. This work suggests the need for further study of the role of heat pumps in flexibility markets and research questions concerning the aggregation of local clusters of such flexible technologies.Comisión Europea 69596

Energy and technological refurbishment of the School of Architecture Valle Giulia, Rome

Modern architecture built in historical urban contexts represents a demanding issue when its energy efficiency should be improved. Indeed, the strongest efforts have to be made to maintain the architectural identity and its harmony with the surrounding cultural heritage. This study deals with the main building of the School of Architecture Valle Giulia in Rome, designed by Enrico Del Debbio in the 30’s. Further constraints are related to several interventions of airspace expansion starting from 1958 which involved the building starting from 1958. So, preservation would mean highlighting its historic change but, adapting the built environment to the contemporary users’ needs. As above-mentioned, the building belongs to the Valle delle Accademie, within the historic park of Villa Borghese, so that to acquire landscaping values. Those latter ones call for ulterior requirements when any new design process is conceived. The study provides a global renewal of the building accounting for the current low Indoor Environmental Quality in both summer and winter seasons and the lack of suitability to the contemporary University student’s needs. The interaction between building performance and HVAC systems was studied by collecting data and architectural surveys conducted by all the architects who modified the building. This procedure was chosen since thermo-physical investigations are considered destructive due to required perforations to identify the actual wall layers. Moreover, thermographic surveys were carried out to validate the modelled building response. The result of the study is the identification of viable interventions to improve the accessibility and fruition of the building as well as its energy performance. A specific cost-benefit analysis was done to prioritize the design options along with considering the measures needed to preserve all the architectural features and values

Integrated model concept for district energy management optimisation platforms

District heating systems play a key role in reducing the aggregated heating and domestic hot water production energy consumption of European building stock. However, the operational strategies of these systems present further optimisation potential, as most of them are still operated according to reactive control strategies. To fully exploit the optimisation potential of these systems, their operations should instead be based on model predictive control strategies implemented through dedicated district energy management platforms. This paper describes a multiscale and multidomain integrated district model concept conceived to serve as the basis of an energy prediction engine for the district energy management platform developed in the framework of the MOEEBIUS project. The integrated district model is produced by taking advantage of co-simulation techniques to couple building (EnergyPlus) and district heating system (Modelica) physics-based models, while exploiting the potential provided by the functional mock-up interface standard. The district demand side is modelled through the combined use of physical building models and data-driven models developed through supervised machine learning techniques. Additionally, district production-side infrastructure modelling is simplified through a new Modelica library designed to allow a subsystem-based district model composition, reducing the time required for model development. The integrated district model and new Modelica library are successfully tested in the Stepa Stepanovic subnetwork of the city of Belgrade, demonstrating their capacity for evaluating the energy savings potential available in existing district heating systems, with a reduction of up to 21% of the aggregated subnetwork energy input and peak load reduction of 24.6%.The research activities leading to the described developments and results, were funded by the European Uniońs Horizon 2020 MOEEBIUS project, under grant agreement No 680517. Authors would like to ex-press their gratitude to the operator of the Vozdovac district heating system (Beogradske elektrane) for the specifications used to develop and calibrate the models, and to Solintel M&P, SL for developing the initial versions of the EnergyPlus models (including only the geometrical and constructive definition of the buildings), in the framework of the MOEEBIUS project

Dynamic simulation model of trans-critical carbon dioxide heat pump application for boosting low temperature distribution networks in dwellings

This research investigates the role of new hybrid energy system applications for developing a new plant refurbishment strategy to deploy small scale smart energy systems. This work deals with a dynamic simulation of trans-critical carbon dioxide heat pump application for boosting low temperature distribution networks to share heat for dwellings. Heat pumps provide high temperature heat to use the traditional emission systems. The new plant layout consists of an air source heat pump, four trans-critical carbon dioxide heat pumps (CO2-HPs), photovoltaic arrays, and a combined heat and power (CHP) for both domestic hot water production and electricity to partially drive the heat pumps. Furthermore, electric storage devices adoption has been evaluated. That layout has been compared to the traditional one based on separated generation systems using several energy performance indicators. Additionally, a sensitivity analysis on the primary energy saving, primary fossil energy consumptions, renewable energy fraction and renewable heat, with changes in building power to heat ratios, has been carried out. Obtained results highlighted that using the hybrid system with storage device it is possible to get a saving of 50% approximately. Consequently, CO2-HPs and hybrid systems adoption could be a viable option to achieve Near Zero Energy Building (NZEB) qualification

District Information Modeling and Energy Management

In recent years the European Commission enhanced strategies to promote ICTs for energy efficiency in buildings and cities. Within the Smart City context, energy-related information coming from different data-sources, either hardware or software needs to be integrated into a common smart digital archive for the city. We propose DIMMER, a distributed software infrastructure for district information modelling and energy management. It correlates energy-related information from different data-sources with user behaviour patterns and feedbacks. Hence, different actors playing in this scenario can access relevant information for providing new services and developing more efficient policies to enhance energy optimization in cities. This will provide support for strategic planning of the city and will foster the competition in the marketplace