Prior Day Effect in Forecasting Daily Natural Gas Flow from Monthly Data

Many needs exist in the energy industry where measurement is monthly yet daily values are required. The process of disaggregation of low frequency measurement to higher frequency values has been presented in this literature. Also, a novel method that accounts for prior-day weather impacts in the disaggregation process is presented, even though prior-day impacts are not directly recoverable from monthly data. Having initial daily weather and gas flow data, the weather and flow data are aggregated to generate simulated monthly weather and consumption data. Linear regression models can be powerful tools for parametrization of monthly/daily consumption models and will enable accurate disaggregation. Two-, three-, four-, and six-parameter linear regression models are built. RMSE and MAPE are used as means for assessing the performance of the proposed approach. Extensive comparisons between the monthly/daily gas consumption forecasts show higher accuracy of the results when the effect of prior-day weather inputs are considered

Disaggregation: Inferring Daily Gas Flow from Billing Cycle Data

Local natural gas distribution companies rely on accurate forecasts of daily demand to buy gas and deliver it to their customers. To forecast consumption, mathematical models with inputs such as weather and historical daily demand are considered. Many needs exist in the energy industry where the frequency of measurement is different from demanded. When the needed forecast frequency is higher than the measurements, disaggregation approaches are needed. We built multi-parameter linear regression models using monthly data. Several decoration methods in the disaggregation process are developed to improve the model accuracy. Prior-day weather adjustment is used to capture the daily fluctuation of gas consumption as a result of the temperature differences between current day and prior day. Also, behavioral patterns in gas consumption are incorporated in the models to account for consumption patterns in weekdays vs. weekend and days of week. Furthermore, we consider long-term characteristics in the gas consumption data originated from population changes, differences in building efficiency, and economic impacts. Firstly, Base Load Trend and later Heat Load Trend are considered in the linear regression models. Secondly, historical flow is detrended to act like the most recent data by altering its characteristics to approximate a stationary customer base with current behavioral patterns. Root Mean Square Error, Mean Absolute Percent Error, and Weighted Mean Absolute Percent Error are used as means for assessing the performance of our approaches. All decorations enhance forecasts, with Prior-Day adjustment as the most effective. The combination of decorations leads to further enhancements. Inclusion of detrending models decreases the forecast errors significantly. For geographic areas that have experienced identifiable trends, considering Base Heat Load Trend in the model shows the most improvement in detrending models. Extensive comparisons between decoration and detrending algorithms and the combination of these models show all methods enhance daily gas demand forecast accuracies. The combination of Base Heat Load Trend model, Day of the Week, and Prior-Day adjustment is most effective to improve the accuracy of daily demand forecasts from historical monthly gas consumption without need to any additional infrastructure to save Local Distribution Companies and customers a large amount of money