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sinfonia:energy_efficiency_in_domestic_electrical_energy_use [2020/07/14 16:20] – [Study: Comparison of Measured Consumption of Refrigeration Appliances and Data from Energy Labelling] gergina.radeva@passiv.desinfonia:energy_efficiency_in_domestic_electrical_energy_use [2021/03/02 05:33] (current) – naman.sukhija@passiv.de
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 In order to do a target-oriented consultation to reduce the energy consumption of households, a tool was developed that allows the listing of all household appliances individually with their power and energy consumptions per usage. Besides the energy-related parameters, the recording of frequency and duration of usage is equally important, as only with the knowledge of power input AND usage the consumption per year can be estimated.  In order to do a target-oriented consultation to reduce the energy consumption of households, a tool was developed that allows the listing of all household appliances individually with their power and energy consumptions per usage. Besides the energy-related parameters, the recording of frequency and duration of usage is equally important, as only with the knowledge of power input AND usage the consumption per year can be estimated. 
  
-[{{ :picopen:section_of_the_energy_efficienca_tool.png?400|Figure 18.: Section from the Energy Efficiency Tool to list all energy consumer individually. Here you can see the input area for white ware including frequency of usage, recommended alternatives and savings per year.}}] +[{{ :picopen:section_of_the_energy_efficienca_tool.png?700|Figure 18.: Section from the Energy Efficiency Tool to list all energy consumer individually. Here you can see the input area for white ware including frequency of usage, recommended alternatives and savings per year.}}] 
 A general assumption for the usage could lead to strayed optimisations in many cases. This applies especially for white ware such as refrigeration appliances, dish washers, washing machines and tumble driers. Depending on the frequency of usage, which e. g. for the tumble dryer can vary between a few times per month to up to several times per day, investing in a new appliance can be absolutely not recommendable or highly profitable. Furthermore the usage and standby periods are recorded and the energy consumption for lighting can be taken into consideration. \\ A general assumption for the usage could lead to strayed optimisations in many cases. This applies especially for white ware such as refrigeration appliances, dish washers, washing machines and tumble driers. Depending on the frequency of usage, which e. g. for the tumble dryer can vary between a few times per month to up to several times per day, investing in a new appliance can be absolutely not recommendable or highly profitable. Furthermore the usage and standby periods are recorded and the energy consumption for lighting can be taken into consideration. \\
  
 The total energy consumption per year has turned out to be an important benchmark for making sure all relevant appliances have been listed. Previous experience has shown good correlation of the overall consumption and the calculated prognosis with a typical deviation of less than 15 %. In addition to the presently used appliances alternatives can be entered and savings can be calculated accordingly. The total energy consumption per year has turned out to be an important benchmark for making sure all relevant appliances have been listed. Previous experience has shown good correlation of the overall consumption and the calculated prognosis with a typical deviation of less than 15 %. In addition to the presently used appliances alternatives can be entered and savings can be calculated accordingly.
 +\\ \\ \\ 
 === Monitoring of the Electric Energy Consumption === === Monitoring of the Electric Energy Consumption ===
  
 In addition to the energy consumption the user profiles can be determined from the monitoring results. The following figure shows the measured energy consumption of individual appliances in one exemplary household. In combination with the information from the consultation tool this results in a potential overall reduction of 30 %.  In addition to the energy consumption the user profiles can be determined from the monitoring results. The following figure shows the measured energy consumption of individual appliances in one exemplary household. In combination with the information from the consultation tool this results in a potential overall reduction of 30 %. 
  
-[{{ :picopen:measured_consumption_curves.jpg?400|Figure 19.: Overview over measured consumption curves of various consumers in a household (left). Next to it the current energy consumption and the expected consumption after all recommended measures form the consultation put into action with an overall reduction of 30 % (right).}}]+[{{ :picopen:measured_consumption_curves.jpg?700|Figure 19.: Overview over measured consumption curves of various consumers in a household (left). Next to it the current energy consumption and the expected consumption after all recommended measures form the consultation put into action with an overall reduction of 30 % (right).}}]
  
 Based on the available data an economic evaluation of the viability of optimisations was performed. The assessment was based on a life cycle cost approach. Finally the household electrical energy efficiency class rating for actual and optimised configuration are also shown. \\ Based on the available data an economic evaluation of the viability of optimisations was performed. The assessment was based on a life cycle cost approach. Finally the household electrical energy efficiency class rating for actual and optimised configuration are also shown. \\
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 **All results and recommendations thus obtained are wrapped up on a results sheet and explained to the participants comprehensively and in person. This is considered a very important factor for the success of the approach as it builds confidence as well as understanding.** \\ **All results and recommendations thus obtained are wrapped up on a results sheet and explained to the participants comprehensively and in person. This is considered a very important factor for the success of the approach as it builds confidence as well as understanding.** \\
  
-[{{:picopen:energy_consulting_results_sheet.png?400|Figure 20.: Energy consulting results sheet comparing actual and optimised electricity use, the saving potential, pointing out recommended improvements and their economic benefit}}]+[{{:picopen:energy_consulting_results_sheet.png?500|Figure 20.: Energy consulting results sheet comparing actual and optimised electricity use, the saving potential, pointing out recommended improvements and their economic benefit}}]
  
 ===== Experiments with Real-Time Feedback on Energy Use to the Tenants ===== ===== Experiments with Real-Time Feedback on Energy Use to the Tenants =====
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 === Sample 01 === === Sample 01 ===
  
-[{{ :picopen:sample_01.png?400|Figure 21.: Sample 01, overview of metered consumption, linearised for the experiment phases}}] [{{ :picopen:gradients_of_all_experiment_phase.jpg?400|Table 2: Gradients for all experiment phases, average kWh per day}}]+[{{ :picopen:sample_01.png?700|Figure 21.: Sample 01, overview of metered consumption, linearised for the experiment phases}}] [{{ :picopen:gradients_of_all_experiment_phase.jpg?700|Table 2: Gradients for all experiment phases, average kWh per day}}]
 Data sample 01 was acquired in a three-person household within a multifamily building, domestic hot water was prepared with an electric resistance heater. Coincidentally, data acquisition ended on the date when the CoVID19-related lockdown commenced. \\ Data sample 01 was acquired in a three-person household within a multifamily building, domestic hot water was prepared with an electric resistance heater. Coincidentally, data acquisition ended on the date when the CoVID19-related lockdown commenced. \\
  
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 === Sample 02 === === Sample 02 ===
  
- [{{ :picopen:sample_02.png?400|Figure 22.: Sample 02, overview of metered consumption, linearised for the experiment phases}}]  [{{ :picopen:gradients_of_all_experiment_phase_2.jpg?400|Table 3: Gradients for all experiment phases, average kWh per day}}]+ [{{ :picopen:sample_02.png?700|Figure 22.: Sample 02, overview of metered consumption, linearised for the experiment phases}}]  [{{ :picopen:gradients_of_all_experiment_phase_2.jpg?700|Table 3: Gradients for all experiment phases, average kWh per day}}]
 Data sample 02 was acquired in a three-person household within a multifamily building, domestic hot water is prepared with a gas boiler. Coincidentally, data acquisition ended on the date when the CoVID19-related lockdown commenced. \\ Data sample 02 was acquired in a three-person household within a multifamily building, domestic hot water is prepared with a gas boiler. Coincidentally, data acquisition ended on the date when the CoVID19-related lockdown commenced. \\
  
 The total consumption in the time of the experiment amounted to a good 600 kWh in sample 02, which extrapolates to around 2400 kWh per annum. This is a household with very low energy consumption, without electrical DHW preparation. \\ The total consumption in the time of the experiment amounted to a good 600 kWh in sample 02, which extrapolates to around 2400 kWh per annum. This is a household with very low energy consumption, without electrical DHW preparation. \\
  
-It is obvious from the gradients given in table 3 that feeding back information on the energy consumption had no reduction effect in the case of sample 02. On the contrary, the average daily consumption has slightly increased within the feedback phase. However it decreased again in phase 3 and the variation was minimal. Essentially the energy use has been unaffected by the changing boundary conditions of the different experimental phases, despite the advent of spring.  \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\+It is obvious from the gradients given in table 3 that feeding back information on the energy consumption had no reduction effect in the case of sample 02. On the contrary, the average daily consumption has slightly increased within the feedback phase. However it decreased again in phase 3 and the variation was minimal. Essentially the energy use has been unaffected by the changing boundary conditions of the different experimental phases, despite the advent of spring.  \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\
  
  
 === Sample 03 === === Sample 03 ===
-[{{ :picopen:sample_03.png?400|Figure 23.: Sample 03, overview of metered consumption, linearised for the experiment phases}}] [{{ :picopen:gradients_of_all_experiment_phase_3.jpg?400|Table 4: Gradients for all experiment phases, average kWh per day}}]+[{{ :picopen:sample_03.png?700|Figure 23.: Sample 03, overview of metered consumption, linearised for the experiment phases}}] [{{ :picopen:gradients_of_all_experiment_phase_3.jpg?700|Table 4: Gradients for all experiment phases, average kWh per day}}]
  
 Data sample 03 was acquired in a three-person household within a multifamily building, domestic hot water is prepared with a gas boiler. Data acquisition continued for about 10 days onto the period of the CoVID19-related lockdown. \\ Data sample 03 was acquired in a three-person household within a multifamily building, domestic hot water is prepared with a gas boiler. Data acquisition continued for about 10 days onto the period of the CoVID19-related lockdown. \\
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 The total consumption in the time of the experiment amounted to a good 3000 kWh in sample 03, which extrapolates to around 10000 kWh per annum. This is a household with extremely high energy consumption, without electrical DHW preparation included in the data. \\ The total consumption in the time of the experiment amounted to a good 3000 kWh in sample 03, which extrapolates to around 10000 kWh per annum. This is a household with extremely high energy consumption, without electrical DHW preparation included in the data. \\
  
-It is obvious from the gradients given in table 4 that feeding back information on the energy consumption had a persistent reduction effect in the case of sample 03. It held into phase 3, however, a slight reduction in energy consumption might also be attributed to the advent of spring and changed habits due to the CoVID19-related lockdown. \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\+It is obvious from the gradients given in table 4 that feeding back information on the energy consumption had a persistent reduction effect in the case of sample 03. It held into phase 3, however, a slight reduction in energy consumption might also be attributed to the advent of spring and changed habits due to the CoVID19-related lockdown. \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\
  
  
-=== Sample 04 === 
-[{{ :picopen:sample_04.png?400|Figure 24.: Sample 04, overview of metered consumption, linearised for the experiment phases}}] [{{ :picopen:gradients_of_all_experiment_phase_4.jpg?400|Table 5: Gradients for all experiment phases}}] 
  
 +
 +[{{ :picopen:sample_04.png?700|Figure 24.: Sample 04, overview of metered consumption, linearised for the experiment phases}}] [{{ :picopen:gradients_of_all_experiment_phase_4.jpg?700|Table 5: Gradients for all experiment phases}}]
 +\\ \\ \\ \\ \\ \\ \\ \\ \\
 +=== Sample 04 ===
 Data sample 04 was acquired in a two-person household within a multifamily building, domestic hot water preparation was electric but metered separately and is thus not part of the shown data. Data acquisition continued for about five weeks onto the period of the CoVID19-related lockdown. \\ Data sample 04 was acquired in a two-person household within a multifamily building, domestic hot water preparation was electric but metered separately and is thus not part of the shown data. Data acquisition continued for about five weeks onto the period of the CoVID19-related lockdown. \\
  
 The total consumption in the time of the experiment amounted to a good 1600 kWh in sample 04, which extrapolates to around 6500 kWh per annum. This is another household with extremely high energy consumption, without electrical DHW preparation included in the data. \\ The total consumption in the time of the experiment amounted to a good 1600 kWh in sample 04, which extrapolates to around 6500 kWh per annum. This is another household with extremely high energy consumption, without electrical DHW preparation included in the data. \\
  
-It is obvious from the gradients given in table 5 that feeding back information on the energy consumption had a slight reduction effect in the case of sample 04. It held into phase 3, however, a slight reduction in energy consumption might also be attributed to the advent of spring and changed habits due to the CoVID19-related lockdown. Since the latter covered a substantial part of the experiment’s time added uncertainty must be assumed as to the effect of changed habits. \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\+It is obvious from the gradients given in table 5 that feeding back information on the energy consumption had a slight reduction effect in the case of sample 04. It held into phase 3, however, a slight reduction in energy consumption might also be attributed to the advent of spring and changed habits due to the CoVID19-related lockdown. Since the latter covered a substantial part of the experiment’s time added uncertainty must be assumed as to the effect of changed habits. \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\
  
 === Daytime Energy Use Incentives Phase === === Daytime Energy Use Incentives Phase ===
  
-[{{ :picopen:average_hourly_load_curve.png?400|Figure 25.: Average Hourly Load Curve for Households according to [Zimmermann_2012]}}]+[{{ :picopen:average_hourly_load_curve.png?700|Figure 25.: Average Hourly Load Curve for Households according to [Zimmermann_2012]}}]
 According to the literature, the usual temporal distribution of electricity use as observed in the field is not optimally aligned with the PV generation characteristics and peaks in the early morning and, particularly, in the evening hours (cf. figure, exemplary taken from [Zimmermann_2012]). \\ According to the literature, the usual temporal distribution of electricity use as observed in the field is not optimally aligned with the PV generation characteristics and peaks in the early morning and, particularly, in the evening hours (cf. figure, exemplary taken from [Zimmermann_2012]). \\
  
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 The typical daily load curve for households is well known from various studies. The above figure illustrates this with a chart from [Zimmermann_2012]. During the small hours of the night the load is low, with a steep increase in the morning when people get up and start their daily routine. After this rush hour a slight decrease and a minor peak around lunch time can be observed. The main activities only begin after four in the afternoon when inhabitants return home, cook meals, and start electronic entertainment equipment. \\ The typical daily load curve for households is well known from various studies. The above figure illustrates this with a chart from [Zimmermann_2012]. During the small hours of the night the load is low, with a steep increase in the morning when people get up and start their daily routine. After this rush hour a slight decrease and a minor peak around lunch time can be observed. The main activities only begin after four in the afternoon when inhabitants return home, cook meals, and start electronic entertainment equipment. \\
  
-Five data logging systems were deployed in late 2019/early 2020 of which three provided data with sufficient quality for hourly evaluation. \\ \\ \\ \\ \\+Five data logging systems were deployed in late 2019/early 2020 of which three provided data with sufficient quality for hourly evaluation. 
  
-[{{:picopen:sample_01_feedback.png?400|Figure 26.: Sample 01, Load curve for Feedback Phase of Experiment. Boxplot with Quartiles.}}] [{{:picopen:sample_01_special_tariff.png?400|Figure 27.: Sample 01, Load curve for Special Tariff  Phase of Experiment. Boxplot with Quartiles.}}]+ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ 
 +[{{:picopen:sample_01_feedback.png?500|Figure 26.: Sample 01, Load curve for Feedback Phase of Experiment. Boxplot with Quartiles.}}] [{{:picopen:sample_01_special_tariff.png?500|Figure 27.: Sample 01, Load curve for Special Tariff  Phase of Experiment. Boxplot with Quartiles.}}]
  
 In sample 01 it seems that the variability of electricity use differed somewhat in the two consecutive experiment phases but the general pattern of the median hourly share in the daily total appears to exhibit no significant change. The median curve largely follows the characteristics determined by other studies, e.g. [Zimmermann_2012]. There is no indication that daytime energy use had been incentivised in a significant way by the daytime price. \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ In sample 01 it seems that the variability of electricity use differed somewhat in the two consecutive experiment phases but the general pattern of the median hourly share in the daily total appears to exhibit no significant change. The median curve largely follows the characteristics determined by other studies, e.g. [Zimmermann_2012]. There is no indication that daytime energy use had been incentivised in a significant way by the daytime price. \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\
  
-[{{:picopen:sample_02_feedback.png?400|Figure 28.: Sample 02, Load curve for Feedback Phase of Experiment. Boxplot with Quartiles.}}] +\\ \\ 
-[{{:picopen:sample_02_special_tariff.png?400|Figure 29.: Sample 02, Load curve for Special Tariff  Phase of Experiment. Boxplot with Quartiles.}}]+[{{:picopen:sample_02_feedback.png?500|Figure 28.: Sample 02, Load curve for Feedback Phase of Experiment. Boxplot with Quartiles.}}] 
 +[{{:picopen:sample_02_special_tariff.png?500|Figure 29.: Sample 02, Load curve for Special Tariff  Phase of Experiment. Boxplot with Quartiles.}}]
 In sample 02 it seems that the variability of electricity use is pronounced in this household and differed less than in sample 01 in the two consecutive experiment phases. The general pattern of the median hourly share in the daily total, however, still follows the average profile rather well. One notable difference, for both phases of the experiment is, that in this household the morning hump is more pronounced than the evening hump. If a change in user behaviour can be observed it is a shift of the energy use towards the evening but there are no signs for increased daytime energy use. In sample 02 it seems that the variability of electricity use is pronounced in this household and differed less than in sample 01 in the two consecutive experiment phases. The general pattern of the median hourly share in the daily total, however, still follows the average profile rather well. One notable difference, for both phases of the experiment is, that in this household the morning hump is more pronounced than the evening hump. If a change in user behaviour can be observed it is a shift of the energy use towards the evening but there are no signs for increased daytime energy use.
  
  \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\  \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\
-[{{:picopen:sample_03_feedback.png?400|Figure 30.: Sample 03, Load curve for Feedback Phase of Experiment. Boxplot with Quartiles}}] +[{{:picopen:sample_03_feedback.png?500|Figure 30.: Sample 03, Load curve for Feedback Phase of Experiment. Boxplot with Quartiles}}] 
-[{{:picopen:sample_03_special_tariff.png?400|Figure 31.: Sample 03, Load curve for Special Tariff  Phase of Experiment. Boxplot with Quartiles.}}]+[{{:picopen:sample_03_special_tariff.png?500|Figure 31.: Sample 03, Load curve for Special Tariff  Phase of Experiment. Boxplot with Quartiles.}}]
  
 In sample 03 both the variability of electricity use and the hourly share during the daytime hours seem to have increased from one phase of the experiment to the next. One notable difference to the average profile, for both phases of the experiment is, that in this household the morning hump is more pronounced than the evening hump. In this sample significant signs for increased daytime energy use can be observed. \\ In sample 03 both the variability of electricity use and the hourly share during the daytime hours seem to have increased from one phase of the experiment to the next. One notable difference to the average profile, for both phases of the experiment is, that in this household the morning hump is more pronounced than the evening hump. In this sample significant signs for increased daytime energy use can be observed. \\
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 \\ \\
 This project has received funding from the European Union’s Seventh Programme for research, technological development and demonstration under grant agreement No 609019.//  This project has received funding from the European Union’s Seventh Programme for research, technological development and demonstration under grant agreement No 609019.// 
 +
 +
 +----
 +//Energy labels mentioned in the article refer to the EU labels applicable before 01 March 2021.//
  
  
  
    
sinfonia/energy_efficiency_in_domestic_electrical_energy_use.1594736449.txt.gz · Last modified: 2020/07/14 16:20 by gergina.radeva@passiv.de