Mathematical Models for Natural Gas Forecasting

It is vital for natural gas Local Distribution Companies (LDCs) to forecast their customers\u27 natural gas demand accurately. A significant error on a single very cold day can cost the customers of the LDC millions of dollars. This paper looks at the financial implication of forecasting natural gas, the nature of natural gas forecasting, the factors that impact natural gas consumption, and describes a survey of mathematical techniques and practices used to model natural gas demand. Many of the techniques used in this paper currently are implemented in a software GasDayTM, which is currently used by 24 LDCs throughout the United States, forecasting about 20% of the total U.S. residential, commercial, and industrial consumption. Results of GasDay\u27sTM forecasting performance also is presented

Disaggregating time series data for energy consumption by aggregate and individual customer

This dissertation generalizes the problem of disaggregating time series data and describes the disaggregation problem as a mathematical inverse problem that breaks up aggregated (measured) time series data that is accumulated over an interval and estimates its component parts. We describe five different algorithms for disaggregating time series data: the Naive, Time Series Reconstruction (TSR), Piecewise Linear Optimization (PLO), Time Series Reconstruction with Resampling (RS), and Interpolation (INT). The TSR uses least squares and domain knowledge of underlying correlated variables to generate underlying estimates and handles arbitrarily aggregated time steps and non-uniformly aggregated time steps. The PLO performs an adjustment on underlying estimates so the sum of the underlying estimated data values within an interval are equal to the aggregated data value. The RS repeatedly samples a subset of our data, and the fifth algorithm uses an interpolation to estimate underlying estimated data values. Several methods of combining these algorithms, taken from the forecasting domain, are applied to improve the accuracy of the disaggregated time series data. We evaluate our component and ensemble algorithms in three different applications: disaggregating aggregated (monthly) gas consumption into disaggregated (daily) gas consumption from natural gas regional areas (operating areas), disaggregating United States Gross Domestic Product (GDP) from yearly GDP to quarterly GDP, and forecasting when a truck should fill a customer's heating oil tank. We show our five algorithms successfully used to disaggregate historical natural gas consumption and GDP, and we show combinations of these algorithms can improve further the magnitude and variability of the natural gas consumption or GDP series. We demonstrate that the PLO algorithm is the best of the Naive, TSR, and PLO algorithms when disaggregating GDP series. Finally, ex-post results using the Naive, TSR, PLO, RS, INT, and the ensemble algorithms when applied to forecast heating oil deliveries are shown. Results show the Equal Weight (EW) combination of the Naive, TSR, PLO, RS, and INT algorithms outperforms the forecasting system Company YOU used before approaching the GasDay™laboratory at Marquette University, and comes close, but does not outperform existing techniques the GasDay™ laboratory has implemented to forecast heating oil deliveries

Detrending Daily Natural Gas Demand Data Using Domain Knowledge

Natural gas Local Distribution Companies (LDCs) need to estimate their customers’ gas demand accurately. A significant factor in the process of forecasting gas demand is historical data. This article presents a method of detrending historical data using linear regression and mathematical modeling applied to natural gas consumption. We present a detailed explanation of detrending the historical data with an annual two‐parameter model and a five‐parameter model at higher frequency. Our goal is to make all historical data look like it occurred during the most recent heating season. By using this method, demands from heating seasons before the most recent can be adjusted by adding demand proportional to the difference in the model terms so that historical daily data for different years become approximately stationary. The benefit of this detrending is demonstrated with an example of building forecast models with and without detrending on different length training sets

Detrending Daily Natural Gas Consumption Series to Improve Short-Term Forecasts

This paper presents a novel detrending algorithm that allows long-term natural gas demand signals to be used effectively to generate high quality short-term natural gas demand forecasting models. Short data sets in natural gas forecasting inadequately represent the range of consumption patterns necessary for accurate short-term forecasting. In contrast, longer data sets present a wide range of customer characteristics, but their long-term historical trends must be adjusted to resemble recent data before models can be developed. Our approach detrends historical natural gas data using domain knowledge. Forecasting models trained on data detrended using our algorithm are more accurate than models trained using nondetrended data or data detrended by benchmark methods. Forecasting accuracy improves using detrended longer-term signals, while forecast accuracy decreases using non-detrended long-term signals