*4.3. The Temporal Profiles of Energy Consumption in Manhattan*

Figure 8 illustrates similar seasonal electricity-use intensity patterns but different seasonal gas-use intensity patterns. Specifically, the electricity-use intensity is very similar in four seasons except in summer. In addition to the general electricity use, electricity for cooling purposes is another major electricity consumption source in the summer. Therefore, a slight electricity-use intensity increase could be found in the summer. Significantly different seasonal gas-use intensity patterns could be found for Manhattan. In particular, gas-use intensity is the highest in the winter for heating purposes and much lower in the other three seasons. Moreover, spatially varied patterns of gas-use intensity could also be found in Figure 8. The central area and southern corner of Manhattan have a little bit lower gas-use intensity in the winter as some buildings are renovated with improved HVAC systems or built after 1980 with improved energy-use efficiency.

**Figure 8.** Seasonal energy-use intensity in Manhattan, NYC, in 2012.

To examine the monthly profiles of the building energy use in Manhattan, I aggregated the hourly electricity and gas consumption to the monthly scale. Figure 9 illustrates strong monthly electricityand gas-use variations. Both residential and commercial buildings show only one peak for both electricity and gas consumption in 2012. Specifically, the peak of electricity use is in the summer (around July and August) due to high cooling demand, and the peak of gas consumption is in the winter (around December and January) owing to high heating demand. In particular, the electricity use is stable from January to April and October to December; it starts jumping up in May, reaching the peak in July and August owing to high cooling demand, and finally drops in September. The gas consumption is relatively stable from May to September but starts jumping in October, reaching a peak in December and January due to high heating demand, and finally drops in April.

**Figure 9.** Monthly electricity and gas consumption in Manhattan in 2012.

Figures 10 and 11 illustrate hourly building energy use and energy-use intensity for the hottest (07-18-2012) and coldest (01-04-2012) days in Manhattan in 2012. The summer energy-use profiles show only one peak around noon, and the summer peak is mainly contributed by the high cooling demand from both residential and commercial buildings as it is the hottest time during a day. It is very consistent with the hourly building occupancy schedule provided in Figure 5 and hourly energy-use intensity provided in Figure 11. Most buildings such as offices, schools, retail stores, and supermarkets are opening around 7 or 8 a.m. in the morning, with the highest occupancy around noon from 11 a.m. to 1 p.m. The summer energy-use peak is contributed by both high building occupancy and high outside temperature. Figure 10 also shows a low demand for gas consumption as gas is only used for water heating at this time. In winter, energy consumption is much higher than in the summer. Two peaks could be found in Figures 10 and 11, with one significant peak in the morning and another peak in the evening. The first peak is mainly caused by high heating demand from commercial buildings. The residential building occupancy in Figure 5 shows that people are leaving home around 7 a.m. and most commercial buildings, such as offices, retail stores, and supermarkets, open at 7 or 8 a.m. It does support the conclusion that the first-morning energy-use peak is caused by high heating demand in the morning. The second peak is mainly contributed by residential buildings, as illustrated in Figure 5, in that people finish their work and return home around 6 p.m., and a high heating demand was caused by low outside temperatures.

Figure 11 also shows the improvement in energy-use efficiency for buildings built after 1980. Significant energy-use reduction could be found for some specific building types. For building electricity consumption, the strip mall, large office, and secondary school have all been improved significantly. The peak electricity-use intensity of strip mall, large office, and secondary school drops from around 0.09 kWh/m<sup>2</sup> to around 0.08 kWh/m2, around 0.06 kWh/m2 to 0.05 kWh/m2, and around 0.04 kWh/m<sup>2</sup> to 0.02 kWh/m2, respectively. Moreover, the efficiency of gas consumption was also improved. The peak gas-use intensity of the secondary schools also drops from around 0.25 kWh/m2 to 0.19 kWh/m2, and the peak gas-use intensity of the strip malls drops from 0.3 kWh/m<sup>2</sup> to 0.2 kWh/m2. A similar situation could also be detected in Figure 9 for other building types. In summary, the energy-use efficiency of buildings built after 1980 has been improved significantly.

*Energies* **2020**, *13*, 3244

**Figure 10.** Hourly building energy use for summer and winter peak days.

**Figure 11.** Hourly energy-use intensity (major building types) in Manhattan, NYC.

#### *4.4. Sensitivity Analysis*

Figure 12 illustrates the significant differences in building energy use between applying the TMY weather data and the localized weather data. In particular, residential buildings have a much higher electricity consumption in the summer and lower gas consumption in the winter in 2012 compared to the TMY as the temperature in 2012 was much higher than in other years. When the TMY weather data are applied, significant underestimation of electricity consumption (up to 16%) and overestimation of gas consumption (up to 24%) occur, due to underestimating the cooling demand and overestimating the heating demand. Similar patterns could also be found for commercial buildings. The application of TMY weather data in building energy modeling could result in underestimation of electricity use (up to 18%) in the summer and overestimation of gas consumption (up to 21%) in the winter, as there is unreasonable cooling and heating demand generated by the TMY weather data. In summary, electricity consumption for cooling and gas consumption for heating are all very important components of building energy consumption. When inappropriate weather data are applied, the building energy consumption will be highly misunderstood. Therefore, it is important to generate and apply localized weather data in building energy-use modeling.

**Figure 12.** Comparative analysis of the modeled energy use by using localized weather data and TMY weather data.

#### **5. Conclusions**

In this study, the building energy-use dynamics of Manhattan, NYC, was modeled through integrating localized weather data and UBEM. Specifically, this study generated localized weather data based on the collected urban physical parameters and observed hourly weather data using UWG. A building energy-use model was established and calibrated for Manhattan, NYC, based on the collected RECS and CBEC reference data. Finally, building energy use was simulated and explored, to observe the spatial and temporal patterns of Manhattan, NYC.

The analysis results suggest several major conclusions. Firstly, the largest building electricity and gas uses are located in the center of Manhattan, which is mainly covered by commercial buildings with the largest building density and height. Secondly, similar seasonal electricity-use patterns and different seasonal gas-use patterns could be found in Manhattan. Specifically, the building electricity use is stable throughout all seasons. The largest gas consumption could be found in the winter due to high heating demand and low gas consumption in the summer as the gas is only used for water heating and cooking purpose. Thirdly, the summer energy use hourly profiles show only one peak for electricity use, mainly contributed by the high cooling demand. Winter energy use hourly profiles suggest two gas-use peaks. The first one is in the morning as people started working with high heating demand, and the second peak is associated with high heating demand from residential buildings when people finish their daily work and get back home.

While building energy use has been improved with localized weather data, there are still some other issues that need to be considered in the future, such as including the economic activity in the energy-use model. This study only modeled building energy use in the past. However, the understanding of future building energy use may be even more important as it could provide reference support for sustainable city planning. In 2014, the Intergovernmental Panel on Climate Change (IPCC) has released the fifth assessment report about future climate change, and the simulated future weather under different socio-development scenarios have been widely used in many studies already [52–54]. Therefore, one possible future research direction could be estimating future building energy use with consideration of both the local microclimate and future climate change under different scenarios. In addition, the same building occupancy schedule was applied in the same building group in this study. However, buildings located in a different part of the city may have different occupancy schedules. Therefore, another future research direction could be improving building energy-use modeling with actual building occupancy schedules extracted from other data sources, such as socio-media data (e.g., Twitter. Facebook, etc.). Moreover, more accurate reference data is needed to improve the model performance. In this study, only the RECS and CBECS data from EIA in 2009 and 2012 were used as the reference data for model calibration. While the calibration performance is acceptable, the collected RECS and CBECS data are not very recent data; thus, the calibrated model may not be able to consider the current energy use conditions as impacted by the economy. The EIA is going to release new data in the future. The model could be updated later with new recent data to improve the performance. Moreover, the RECS and CBECS data are reported only at the regional level and the calibrated model may have a much better reflection of energy use at the regional level instead of the individual building level. In addition, the spatial information of the reference data from RECS and CBECS has been blocked to for privacy purposes. Only one energy-use model can be calibrated for one building type, and the spatial variation in energy use of each building type was ignored. When smart-metered utility data become available, the proposed model can be updated and improved for better modeling of building energy use at the individual building level, with consideration of the spatial variation in energy use within each building type.

**Funding:** This research was supported by the Faculty First Award and Sustainability Faculty Fellowship from the University of North Carolina at Greensboro.

**Acknowledgments:** I would like to thank three reviewers for their constructive suggestions on an earlier version of this manuscript.

**Conflicts of Interest:** The author declares no conflict of interest.
