Can natural gas supply keep up with demand for power?
Things are looking up for the energy industry, but tough issues...
combined HDD and CDD into a single-time series of total degree-days (TDD), and the results are shown in Figure 2. The total consumption of natural gas in Arizona shows peaks of similar magnitude for both the heating and cooling seasons. The TDD plot shows an exaggerated cooling season and almost no winter increase in demand. The EDD totals more accurately reflect the aggregate consumption of natural gas by capturing the upturn in winter demand and more accurately representing the relative strengths of the heating and cooling seasons. To quantify this improvement, you can look at the change in the correlation coefficients. The weather-to-consumption correlation coefficient improves from 0.31 to 0.49 using the EDD series over the standard TDD results. As a result, a predictive model that estimates gas consumption from current weather using our EDD totals would allow one to better track the available supplies and usage.
A critical factor for some applications in understanding the regional market is timing the transition from heating to cooling season. Using operational capacity data from the El Paso Gas Pipeline for flow points in Arizona, Figure 3 shows the monthly capacity utilization percent against the same definitions of TDD and EDD. In this instance, a comparison shows a switch from a negative to a positive correlation (-0.425 to 0.401). The negative correlation for the standard degree day definition implies incorrectly that pipeline utilization decreases with extreme temperatures. By correctly matching the peaks and valleys of the capacity utilization time-series with EDD, we can see at a more localized level the impact of weather on gas demand and a more accurate representation of seasonal transitions.
We have determined that the use of EDD or a similar technique will improve weather and demand correlation in almost all energy applications. By defining the neutral heating temperature to be a fundamental property of the local temperature/demand relationship, we normalize the data in a way that improves linear analysis techniques when compared with traditional degree-day measures.
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