**4.2 Robust design**

Therefore, there are many uncertain parameters which may cause great performance discrepancy between the design stage and operation stage of ZEB. The impact of uncertain parameters on system selection can be illustrated in **Figure 11**. In convention building, since it has no constraint on the mismatch between building energy consumption and on-site generation, the optimal design option O is usually selected within all the design options (Area of A), which is located below the net-zero balance line with 100% confidence level. When designing ZEB using deterministic approach, the constraint of annual energy balance is achieved for the design year, and thus the optimal design option O′ is usually selected within a few design options (Area of B), which is located on the net-zero balance line with approximately 50% confidence level. When uncertain parameters are concerned for a robust ZEB design, a narrowed area of C is identified as the selected option is required to satisfy many years of operation. Therefore, the optimal design option O″ is selected on/above the net-zero balance line with 100% confidence level. In the study of Lu [36], the RES size should be selected with a mismatch ratio of about 30% to ensure a probability of 100% of being ZEB in different years.

#### **4.3 Impact of key parameters on ZEB performance**

As mentioned in **Figure 12**, various parameters can affect ZEB system selection and performance. Six key parameters are selected here and grouped in pairs according to their specifications, i.e., PV price (ranges between 1200 and 2000 \$/kW) and

#### **Figure 11.**

*Impact of uncertain parameters on ZEB performance. Note: A, all design options; B, design options for nZEB based on deterministic condition; C, design options for nZEB under uncertainties; O/O*′*/O*″*, optimal design option.*

**51**

**Figure 12.**

120 m2

**5. Conclusion**

standard.

*Definition and Design of Zero Energy Buildings DOI: http://dx.doi.org/10.5772/intechopen.80708*

*Variations of NPC and GII under the effect of two factors (Note: The point B is the value under basic case.).*

electricity price, sellback price, demand load ratio and the type of ZEB are identified to be more important on NPC than PV and CONV price. It should be noted that the results may be different when applied for different parameter variation ranges.

This chapter aims to present a comprehensive view of the key issues related to the development of zero energy building including the definition, supporting incentives, evaluation criteria, design methodologies, and uncertainty analysis. Although a wide range of researches can be found to investigate one aspect of the ZEB study, there are still a lot of challenges faced to be solved in the future:

1.A consensus definition and interpretation of national/regional ZEB should be further declared; then, the design/control strategies with the corresponding performance of ZEB can be investigated and compared under the same

2.Since a lot of factors/parameters can cause the discrepancies between predicted and realized target and ZEB performance, it should be noted that even the same factors may have a different effect on ZEB design for a specified

converter price (ranges between 400 and 800 \$/kW), electricity price (EP) (ranges between 0.04 and 0.12 \$/kWh) and sellback price (SP) (ranges between 0.04 and 0.12 \$/kWh), and demand load ratio (ranges between 0.8 and 1.2) and the type of ZEB (ranges between 0.2 and 1.0), respectively. The impact of the group in pairs on NPC and GII is compared based on a hypothetical residential house (an area of

) that is located in Shanghai, China. Under the selected ranges of parameters,

*Definition and Design of Zero Energy Buildings DOI: http://dx.doi.org/10.5772/intechopen.80708*

*Green Energy Advances*

identified.

**4.2 Robust design**

being ZEB in different years.

**4.3 Impact of key parameters on ZEB performance**

are the main parameters affecting building load.

In terms of building energy load, building design parameters (e.g., indoor set temperature and humidity, thermal insulation of external walls, window area, etc.), operational parameters (e.g., outdoor temperature and humidity, solar radiation, etc.), and energy system efficiency (e.g., lighting efficiency, HVAC efficiency, etc.)

Economic parameters including the price of RES, feed-in tariff, and electricity price from/to the grid are also key parameters affecting RES selection and ZEB performance. In addition, many researches have found that both the building energy load and the local renewable resources are different even for the same building located in different climate zones, which indicate that different key design parameters may exist for different climate zones and should be further

Therefore, there are many uncertain parameters which may cause great performance discrepancy between the design stage and operation stage of ZEB. The

As mentioned in **Figure 12**, various parameters can affect ZEB system selection and performance. Six key parameters are selected here and grouped in pairs according to their specifications, i.e., PV price (ranges between 1200 and 2000 \$/kW) and

*Impact of uncertain parameters on ZEB performance. Note: A, all design options; B, design options for nZEB based on deterministic condition; C, design options for nZEB under uncertainties; O/O*′*/O*″*, optimal design* 

impact of uncertain parameters on system selection can be illustrated in **Figure 11**. In convention building, since it has no constraint on the mismatch between building energy consumption and on-site generation, the optimal design option O is usually selected within all the design options (Area of A), which is located below the net-zero balance line with 100% confidence level. When designing ZEB using deterministic approach, the constraint of annual energy balance is achieved for the design year, and thus the optimal design option O′ is usually selected within a few design options (Area of B), which is located on the net-zero balance line with approximately 50% confidence level. When uncertain parameters are concerned for a robust ZEB design, a narrowed area of C is identified as the selected option is required to satisfy many years of operation. Therefore, the optimal design option O″ is selected on/above the net-zero balance line with 100% confidence level. In the study of Lu [36], the RES size should be selected with a mismatch ratio of about 30% to ensure a probability of 100% of

**50**

**Figure 11.**

*option.*

**Figure 12.** *Variations of NPC and GII under the effect of two factors (Note: The point B is the value under basic case.).*

converter price (ranges between 400 and 800 \$/kW), electricity price (EP) (ranges between 0.04 and 0.12 \$/kWh) and sellback price (SP) (ranges between 0.04 and 0.12 \$/kWh), and demand load ratio (ranges between 0.8 and 1.2) and the type of ZEB (ranges between 0.2 and 1.0), respectively. The impact of the group in pairs on NPC and GII is compared based on a hypothetical residential house (an area of 120 m2 ) that is located in Shanghai, China. Under the selected ranges of parameters, electricity price, sellback price, demand load ratio and the type of ZEB are identified to be more important on NPC than PV and CONV price. It should be noted that the results may be different when applied for different parameter variation ranges.
