Montana Kaimin, October 23, 2008

Student newspaper of the University of Montana, Missoula.https://scholarworks.umt.edu/studentnewspaper/6217/thumbnail.jp

Analysis of an integrated heating and cooling system for a building complex with focus on long–term thermal storage

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A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings

This paper presents a variation focused cluster analysis strategy to identify typical daily heating energy usage profiles of higher education buildings. Different from the existing cluster analysis studies which were primarily developed using Euclidean distance as the dissimilarity measure and tended to group the daily load profiles with similar magnitudes, Partitioning Around Medoids (PAM) clustering algorithm with Pearson Correlation Coefficient-based dissimilarity measure was used in this study to group the daily load profiles on the basis of the variation similarity. A comparison of the proposed strategy with a k-means cluster analysis with Euclidean distance as the dissimilarity measure was also performed. The performance of the proposed strategy was tested and evaluated using the three-year hourly heating energy usage data collected from 19 higher education buildings in Norway. The results demonstrated the effectiveness of the proposed strategy in identifying the typical daily energy usage profiles. The identified typical heating load profiles provided the information such as the peaks and troughs of the daily heating demand, daily high heating demand period and daily load variation. The identified profiles also helped to categorize multiple buildings into different groups in terms of the similar energy usage behaviors to support further energy efficiency initiatives

Selecting the model and influencing variables for DHW heat use prediction in hotels in Norway

Domestic hot water heat use prediction modelling is an important instrument for increasing energy efficiency in many buildings. This article addressed hourly domestic hot water heat use prediction, using a Norwegian hotel as a case study. Since the information available for buildings may vary, two widespread situations with different input variables were studied. For the first situation, the prediction is based only on data obtained from historical measured domestic hot water heat use. For the second situation, additional variables that affect domestic hot water heat use were applied. These variables were determined using the Wrapper approach. The Wrapper approach showed that factors related to the guests presence have the most significant influence on the domestic hot water heat use in the hotel. Nevertheless, daily data about the number of guests booked at the hotel did not appear to be informative enough for precise hourly modelling. Therefore, to improve the accuracy of the prediction, it was proposed to use an artificial variable. This artificial variable explained the hourly intensity of the guests domestic hot water use. In order to select the best model for the domestic hot water heat use prediction, ten advanced time series and machine learning techniques were tested based on the criteria of models adequacy. For both considered situations, the Prophet model showed the best results with R2 equal to 0.76 for the first situation, and 0.83 the second situation.publishedVersio

Splitting measurements of the total heat demand in a hotel into domestic hot water and space heating heat use

To achieve more efficient energy use in buildings, space heating (SH) and domestic hot water (DHW) heat use should be analysed separately. Unfortunately, in many buildings, the heat meters measure the total heat use only, typically not divided into SH and DHW. This article presented a method for splitting the total heat use into the SH and the DHW. The splitting follows the assumption that the outdoor temperature is the main parameter explaining the hourly SH heat use, while the hourly DHW heat use is not influenced by this parameter. In the article, the modelled SH heat use was extracted from the total heat use based on the energy signature curve and the singular spectrum analysis. Thereafter, from the residuals between the modelled SH heat use and the total heat use, the DHW heat use was identified. The application of the method for the hotel in Norway showed that restored values represented the trends of the measured SH and DHW heat use well. The coefficient of determination (R2) for the modelled SH heat use was 0.97, and 0.76 for DHW. The methodology is useful for obtaining valuable information for monitoring and improving the energy performance of SH and DHW systemspublishedVersio

Documentation of an integrated thermal energy system for a building complex

In large buildings and building complexes, energy use can be reduced by efficient interaction between heating and cooling demands and thermal storage (short and long term storage). This work describes an integrated energy system in Norway which supplied several commercial and residential buildings with heating and cooling. The integrated thermal energy system consisted of heat pumps (~1 MW total cooling capacity), solar thermal collectors (290 m2), district heating connection as well as water tanks (15000 l) and boreholes (62 x 300 m) for thermal energy storage. The water tanks acted as buffer and balanced the mismatch of supply and demand during a day. The seasonal operation modes were chosen depending on the outdoor conditions. In summer, the condenser heat from the cooling systems and the solar collectors was sent to the boreholes. In winter, the heat pumps used the boreholes and the surplus heat from the cooling systems as heat source and delivered heat to the buildings for space heating and domestic hot water. In spring, certain cooling demands could be covered by free-cooling as long as the borehole temperature was low enough. District heating was utilized to lift the temperature for the domestic hot water and also served as backup system. In this work, the system is described in detail and operational data is presented. Improvement suggestions are made which could cut operational costsacceptedVersio

Success factors of energy efficiency measures in buildings in Norway

The aim of the study was to identify factors and parameters, which could contribute to the successful implementation of energy efficiency measures in buildings, and to find which parameters introduce uncertainties in achieving the planned energy savings. A database of 41 buildings was developed for the analysis. The database contained information related to buildings, energy efficiency measures, and energy use over several years. A presentation method for the persistence of the energy efficiency measures was introduced. Through the energy performance contract, energy savings of 30% of the total energy use were suggested on average. The results showed that the success factors of the energy efficiency measures were: previous energy use, project cost, consultant experience and engagement, and implementation of a good operation plan. The persistence of the energy efficiency measures was influenced by the achieved savings in the first year, the guaranty period, and the implementation of the operation measures. Uncertainties in the presented results were induced by the following factors: temperature correction method, difference in reported building area, correctness of the information regarding the implemented measures, and calculation method. The uncertainty due to lack of information or not delivering the operation measures was about 20% of the total energy use