Weather Sensitivity Analysis of Solar-Assisted Heat Pump Systems

1984 ◽  
Vol 106 (2) ◽  
pp. 182-186 ◽  
Author(s):  
M. Ucar ◽  
S. Mittur
Keyword(s):  
Energy Consumption ◽  
Heat Pump ◽  
Energy Use ◽  
Solar Thermal ◽  
Sensitivity Analyses ◽  
Weather Data ◽  
Thermal Systems ◽  
Average Value ◽  
Weather Sensitivity ◽  
Term Energy

In the scientific literature, various weather models have been proposed and used for the simulation of solar thermal systems. The problem of the sensitivity of solar thermal systems to random fluctuations and persistent patterns in weather have been a source of concern and discussion for a long time. A general solution to this problem has proved to be elusive. This paper presents the results of weather sensitivity analyses performed on solar-assisted heat pump systems used in large buildings. The type of systems modeled usually incorporate relatively small collector areas (approximately equal to 5 to 15 percent of the building floor area). A detailed computer program was used for conducting hour-by-hour, zone-by-zone simulations of the buildings and the energy systems. Truncated Fourier series were used to represent weather data. It is concluded that various unusual weather sequences and perturbations do not affect the long-term energy consumption of the building so long as the average value of these variations is constant. Short-term energy consumption was found to be much more sensitive to these variations. It was also found that a uniform change in the average value of the weather functions, does, in fact, substantially influence building energy use.

Role of Indoor Air Distribution in Performance of Heat Pump Systems in Energy-Efficient Residential Buildings

2006 ◽  
Author(s):  
Ali A. Jal-Alzadeh-Azar ◽  
Ren Anderson ◽  
Keith Gawlik
Keyword(s):  
Energy Consumption ◽  
Heat Pump ◽  
Indoor Air ◽  
High Performance ◽  
Energy Use ◽  
Residential Buildings ◽  
Comfort Level ◽  
Air Distribution ◽  
Air Mixing ◽  
Heating Mode

This paper demonstrates the potential impact of indoor air distribution on the energy consumption of central HVAC systems with cognizance of human thermal comfort. The study focuses on a hypothetical high-performance house incorporating a split heat pump system. The air distribution of this building incorporates high sidewall supply-air registers and near-floor, wall-mounted return-air grilles. Heating-mode stratification resulting from this prevalent configuration is a prime example of situations in which challenges regarding energy efficiency, comfort, and ventilation effectiveness emerge. These challenges underline the importance of adopting a comprehensive design strategy for high-performance buildings. Two indoor air distribution scenarios were analyzed: (1) theoretically well mixed and (2) poorly mixed, representing a realistic case. The former scenario was evaluated using an analytical approach, whereas the latter was investigated through computational fluid dynamics (CFD) simulations. For heating mode, the results indicated the presence of a pronounced thermal stratification resulting from poor air mixing. At 50% of the design heating load, for the well-mixed case, the HVAC system energy consumption was significantly higher. Considerably better air distribution performance was observed with cooling mode, in which the relative energy penalty for the well-mixed scenario was noticeably less. In real-world applications where measures must be taken to achieve near perfectly mixed indoor conditions for better comfort, the energy use is expected to be even higher. However, in the absence of such measures, the thermostat setpoint is likely to be readjusted, leading to a higher energy use without necessarily improving the overall comfort level, as demonstrated in this paper. The limitation of increasing the supply-air flow rate to enhance air mixing and diffusion is also discussed in terms of the system moisture removal capability.

scholarly journals Forecasting residential gas consumption with machine learning algorithms on weather data

2019 ◽  
Vol 111 ◽  
pp. 05019
Author(s):  
Brian de Keijzer ◽  
Pol de Visser ◽  
Víctor García Romillo ◽  
Víctor Gómez Muñoz ◽  
Daan Boesten ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Energy Use ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Computational Time ◽  
Percentage Error ◽  
Learning Models ◽  
Gas Consumption ◽  
Machine Learning Models

Machine learning models have proven to be reliable methods in the forecasting of energy use in commercial and office buildings. However, little research has been done on energy forecasting in dwellings, mainly due to the difficulty of obtaining household level data while keeping the privacy of inhabitants in mind. Gaining insight into the energy consumption in the near future can be helpful in balancing the grid and insights in how to reduce the energy consumption can be received. In collaboration with OPSCHALER, a measurement campaign on the influence of housing characteristics on energy costs and comfort, several machine learning models were compared on forecasting performance and the computational time needed. Nine months of data containing the mean gas consumption of 52 dwellings on a one hour resolution was used for this research. The first 6 months were used for training, whereas the last 3 months were used to evaluate the models. The results showed that the Deep Neural Network (DNN) performed best with a 50.1 % Mean Absolute Percentage Error (MAPE) on a one hour resolution. When comparing daily and weekly resolutions, the Multivariate Linear Regression (MVLR) outperformed other models, with a 20.1 % and 17.0 % MAPE, respectively. The models were programmed in Python.

R&D of Thermochemical Reactor Concepts to Enable Seasonal Heat Storage of Solar Energy in Residential Houses

2009 ◽  
Author(s):  
H. A. Zondag ◽  
R. Schuitema ◽  
L. P. J. Bleijendaal ◽  
J. Cot Gores ◽  
V. M. van Essen ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Solar Energy ◽  
The Netherlands ◽  
Heat Recovery ◽  
Fossil Fuels ◽  
Energy Use ◽  
Heat Storage ◽  
Solar Thermal ◽  
Solar Heat ◽  
Seasonal Heat

About 30% of the energy consumption in the Netherlands is taken up by residences and offices. Most of this energy is used for heating purposes. In order to reduce the consumption of fossil fuels, it is necessary to reduce this energy use as much as possible by means of insulation and heat recovery. The remaining demand could be met by solar thermal, provided that an effective way would exist for storing solar heat.

Experimental studies on direct expansion solar thermal heat pump systems using R430A as a substitute to R134a

2021 ◽  
pp. 095440892110209
Author(s):  
M Mohanraj ◽  
I M Kartheheyan
Keyword(s):  
Global Warming ◽  
Energy Consumption ◽  
Heat Pump ◽  
Energy Performance ◽  
Solar Thermal ◽  
Coefficient Of Performance ◽  
Direct Expansion ◽  
Pump System ◽  
Discharge Temperature ◽  
Heating Capacity

The use of halogen-based refrigerants in heat pump applications is restricted because of their high global warming potential (GWP). Therefore, it is necessary to identify a low GWP substitute for heat pump applications. This article presents the energy performance of a direct expansion solar thermal heat pump system (DXSTHPS) using R430A as an environmentally friendly substitute to phase out R134a. The effects of ambient parameters on compressor discharge temperature, compressor energy consumption, condenser heating capacity and coefficient of performance (COP) of a DXSTHPS using R134a and R430A are estimated and compared. Moreover, the total equivalent global warming impacts (TEGWI) of a DXSTHPS using R134a and R430A are evaluated. The results showed that the R430A has 0.7–1.9% lower compressor energy consumption than R134a. The condenser heating capacity and COP of a DXSTHPS using R430A are higher than R134a by 4.6–8.7% and 5.1–10.2%, respectively. The compressor discharge temperature observed in a DXSTHPS using R430A is 5.8 °C higher than R134a. The lubricant physical properties are retained at higher compressor operating temperatures, ensuring compressor reliability. The DXSTHPS using R430A has 4.2–12.9% lower TEGWI due to its lower GWP with lower compressor energy consumption than R134a.

scholarly journals Investigation and prediction of Energy consumption at St. Olavs Hospital

2021 ◽  
Vol 246 ◽  
pp. 04003
Author(s):  
Kristofersen, by Hans Smedsrud ◽  
Kai Xue ◽  
Zhirong Yang ◽  
Liv-Inger Stenstad ◽  
Tor Emil Giske ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Operating Room ◽  
Energy Use ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Heating And Cooling ◽  
Margin Of Error ◽  
The Impact ◽  
Operational Data

The objective of this study is to evaluate and predict the energy use in different buildings during COVID-19 pandemic period at St. Olavs Hospital in Trondheim. Based on machine learning, operational data from St. Olavs hospital combined with weather data will be used to predict energy use for the hospital. Analysis of the energy data showed that the case buildings at the hospital did not have any different energy use during the pandemic this year compared to the same period last year, except for the lab center. The energy consumption of electricity, heating and cooling is very similar both in 2019 and 2020 for all buildings, but in 2020 during the pandemic, the lab center had a reduction of 35% in electricity, compared to last year. An analysis of the energy needed for heating and cooling in the end of June to the end of November was also calculated for operating room 1 and was estimated to 256 kWh/m2 for operation room 1. The machine learning algorithms perform very well to predict the energy consumption of case buildings, Random Forest and AdaBoost proves as the best models, with less than 10% margin of error, some of the models have only 4% error. An analysis of the effect of humidification of ventilation air on energy consumption in operating room 1 was also carried out. The impact on energy consumption were high in winter and will at the coldest periods be able to double the energy consumption needed in the ventilation.

Freight Energy Use Disaggregated by Commodity: Comparisons and Discussion

1998 ◽  
Vol 1641 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Francis M. Vanek ◽  
Edward K. Morlok
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Raw Materials ◽  
High Energy ◽  
Modal Shift ◽  
Commodity Group ◽  
Conservation Policies ◽  
Average Value ◽  
Important Approach ◽  
Intensity Estimate

The analysis of energy consumption in freight transportation is almost always approached by disaggregating overall energy consumption by mode, which then provides a basis for understanding trends and underlying factors that influence them and for developing conservation policies. Another important approach is to disaggregate by commodity, because it is commodity flows that generate the modal vehicle flows and hence the modal energy consumption in transportation. Thus changes in energy use by commodity are important factors in understanding trends in energy consumption and may provide a basis for energy conservation policies centered on industries using transportation. Total freight energy consumption is estimated for a range of commodity groups using an activitybased approach to energy consumption, where total freight activity is decomposed into components by mode and by commodity group, and then each component is multiplied by an intensity estimate to calculate total energy use for that commodity group. Two important findings are discussed: ( a) commodity groups with high energy growth between 1972 and 1993 had a combination of substantial ton-mile growth and modal shift to truck, and ( b) commodity groups of finished products with a high average value per ton in general have a much higher average freight energy intensity than raw materials with a low average value per ton.

scholarly journals Solar Assisted Ground Source Heat Pump Performance in Nearly Zero Energy Building in Baltic Countries

2013 ◽  
Vol 11 ◽  
pp. 48-56 ◽  
Author(s):  
Karolis Januševičius ◽  
Giedrė Streckienė
Keyword(s):  
Heat Pump ◽  
Energy Use ◽  
Residential Buildings ◽  
Hot Water ◽  
Solar Thermal ◽  
Zero Energy ◽  
Pump Design ◽  
Baltic Countries ◽  
Zero Energy Building ◽  
Total Energy Balance

Abstract In near zero energy buildings (NZEB) built in Baltic countries, heat production systems meet the challenge of large share domestic hot water demand and high required heating capacity. Due to passive solar design, cooling demand in residential buildings also needs an assessment and solution. Heat pump systems are a widespread solution to reduce energy use. A combination of heat pump and solar thermal collectors helps to meet standard requirements and increases the share of renewable energy use in total energy balance of country. The presented paper describes a simulation study of solar assisted heat pump systems carried out in TRNSYS. The purpose of this simulation was to investigate how the performance of a solar assisted heat pump combination varies in near zero energy building. Results of three systems were compared to autonomous (independent) systems simulated performance. Different solar assisted heat pump design solutions with serial and parallel solar thermal collector connections to the heat pump loop were modelled and a passive cooling possibility was assessed. Simulations were performed for three Baltic countries: Lithuania, Latvia and Estonia.

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