Each region has a complex interaction between weather, seasonality, and local conditions that goes into generating its own neutral heating temperature.
An example of the temperature-demand curve used to generate the neutral heating temperature is shown in Figure 1, with the typical "U"-shape one expects. The curve for most load regions includes a relatively flat center where the demand for electricity is almost independent of temperature. On either side, as temperatures warm above or drop below the neutral heating temperature, load levels increase sharply. For convenience, we have chosen to fit a relatively simple curve, a second degree polynomial, to this distribution. This allows us to calculate the neutral heating temperature as the bottom of the curve.
This example for Arizona Public Service Co. is a good illustration of why traditional HDD and CDD measures can be misleading. A daily maximum temperature near 65 o F, which for Phoenix probably occurs in December or January, lies very much in the heating portion of the Arizona Public Service curve. It takes significantly warmer temperatures to lead to an increase in demand on the cooling side. It should be noted that you wouldn't necessarily expect the heating and cooling slopes to be the same. For most regions, the cooling slope of the curve is steeper than the heating portion, reflecting that the electrical requirements for heating aren't as demanding as the power draw for cooling. There are regions however, such as upstate New York, where peak demand periods often fall in winter.
Building Better Predictive Tools
Although finding a good way to accurately describe the impact of weather variability on power markets from a historical perspective is important on its own, weather becomes a little more interesting when it comes to predictive applications. Weather, compared to, say, price, is highly predictable, and short-term forecasts are quite good. Even if you extend the time horizon to the seasonal level, you can still find measurable skill for weather prediction in a probabilistic sense. Also, weather data is widely available with good geographic coverage from airports across the country at frequent sampling intervals. This combination lends itself well to using a wide variety of sophisticated analytical methods.
Choosing a local neutral heating temperature as a basis for EDD can be used to improve analysis, develop better predictive tools, and most important, produce superior market decisions. Figure 1 shows that Arizona Public Service is a good example of a regional utility where using 65 o F as the basis for measuring heating and cooling is clearly out of step with demand. Just to the west for San Diego Gas & Electric, a neutral temperature of 65 o F is about right. In the following examples, we explore how using load region EDD can improve energy market analysis.
Weather, Electricity, and Natural Gas Demand in Arizona
Using numbers from , we can look at total monthly consumption of natural gas by state and compare this with both the standard definition of degree days and our EDD values for the 3-year period from 2001-2003. To illustrate the point, we have