Commercial Sector Data Collection

class EnergyIntensityIndicators.commercial.CommercialIndicators(directory, output_directory, level_of_aggregation, lmdi_model=['multiplicative'], end_year=2018, base_year=1985)

Bases: EnergyIntensityIndicators.LMDI.CalculateLMDI

Data Sources: - New construction is based on data from Dodge Data and Analytics. Dodge data on new floor space additions is available from the published versions of the Statistical Abstract of the United States (SAUS). The Most recent data is from the 2020 SAUS, Table 995 “Construction Contracts Started- Value of the Construction and Floor Space of Buildings by Class of Construction: 2014 to 2018”.

activity()

Use logistic parameters to find predicted historical floorspace

adjusted_supplier_data()

This worksheet adjusts some of commercial energy consumption data as reported in the Annual Energy Review. These adjustments are based upon state-by-state analysis of energy consumption in the industrial and commercial sectors. For electricity, there have been a number of reclassifications by utilities since 1990 that has moved sales from the industrial sector to the commercial sector.

The adjustment for electricity consumption is based upon a state-by-state examination of commercial and electricity sales from 1990 through 2011. This data is collected by EIA via Survey EIA-861. Significant discontinuities in the sales data from one year to the next were removed. In most cases, these adjustments caused industrial consumption to increase and commercial consumption to decrease. The spreadsheet with these adjustments is Sectoral_reclassification5.xls (10/25/2012).

In 2009, there was a significant decline in commercial electricity sales in MA and a corresponding increase in industrial sales Assuming that industrial consumption would have fallen by 2% between 2008 and 2009, the adjustment to both the commercial (+) and industrial sectors (-) was estimated to be 7.61 TWh. . The 7.61 TWh converted to Tbtu is 26.0. This value is then added to the negative 164.0 Tbtu in 2009 and subsequent years.

State Energy Data System (Jan. 2017) via National Calibration worksheet

collect_data()

Gather decomposition input data for the Commercial sector

collect_input_data(dataset_name)
collect_weather(comm_activity)

Gather weather data for the Commercial sector

static dod_compare_old()

DODCompareOld Note from PNNL (David B. Belzer): “These series are of unknown origin–need to check Jackson and Johnson 197 (sic)?

dodge_adjustment_ratios(dodge_dataframe, start_year, stop_year, adjust_years, late)

(1985, 1990) or (1960, 1970)

dodge_revised()

Dodge Additions, adjusted for omission of West Census Region prior to 1956

dodge_to_cbecs()

Redefine the Dodge building categories more along the lines of CBECS categories. Constant fractions of floor space are moved among categories.

Returns

redefined data

Return type

dodge_to_cbecs (dataframe)

fuel_electricity_consumption()

Trillion Btu

static get_saus()

Get Data from the Statistical Abstract of the United States (SAUS)

get_seds()

Collect SEDS data

hist_stat()

Historical Dodge Data through 1970

Data Source: Series N 90-100 Historical Statistics of the U.S., Colonial Times to 1970

hist_stat_adj()

Adjust historical Dodge data to account for omission of data for the West Census Region prior to 1956

main(breakout, calculate_lmdi)

Decompose energy use for the Commercial sector

nems_logistic(dataframe, params)
PNNL errors found:

  • Column S in spreadsheet has CO-StatePop2.xls are incorrectly aligned with years

  • Column AL does not actually scale by 1.28 as suggested in the column header

solve_logistic(dataframe)

Solve NES logistic parameters

west_inflation()

Jackson and Johnson Estimate of West Census Region Inflation Factor, West Region Shares Based on CBECS

Note from PNNL: “Staff: Based upon CBECS, the percentage of construction in west census region was slightly greater

in the 1900-1919 period than in 1920-1945. Thus, factor is set to 1.12, approximately same as 1925 value published by Jackson and Johnson”

“

static years_to_str(start_year, end_year)

Create list of year strings from start_year and end_year range