Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression

Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE)

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

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Application of Predictive Models for Natural Gas Needs - Current State and Future Trends Review

Nowadays, in terms of trading on the world scale, to foresee a natural gas consumption represents an essential activity. In the first part, the paper examines the current state of the Serbian natural gas sector and methodology applied for prediction and capacity planning. In addition, the study intends to give a comprehensive assessment of predictive algorithms for natural gas needs involved in the last decade with projections and suggestions for future applications. The primary task is to evaluate used predictive models with an emphasis on the accuracy of the predictions obtained. Additionally, the paper will analyse used parameters, consumption scale, prediction scope, forecast algorithms, and other related information. The main objective of this study is to review the new-fangled information related analyses data from peer-reviewed journals, international conferences, and books

Improving Gas Demand Forecast During Extreme Cold Events

This thesis explores techniques by which the accuracy of gas demand forecasts can be improved during extreme cold events. Extreme cold events in natural gas demand data are associated with large forecast error, which represents high business risk to gas distribution utilities. This work begins by showing patterns associated with extreme cold events observed in natural gas demand data. We present a temporal pattern identification algorithm that identifies extreme cold events in the data. Using a combination of phase space reconstruction and a nearest neighbor classifier, we identify events with dynamics similar to those of an observed extreme event. Results obtained show that our identification algorithm (RPS-kNN) is able to successfully identify extreme cold events in natural gas demand data. Upon identifying the extreme cold events in the data, we attempt to learn the residuals of the gas demand forecast estimated by a base-line model during extreme cold events. The base-line model overforecasts days before and underforecasts days after the coldest day in an extreme cold event due to an unusual response in gas demand to extreme low temperatures. We present an adjustment model architecture that learns the pattern of the forecast residuals and predicts future values of the residuals. The forecasted residuals are used to adjust the initial base model’s estimate to derive a new estimate of the daily gas demand. Results show that the adjustment model only improves the forecast in some instances. Next, we present another technique to improve the accuracy of gas demand forecast during extreme cold events. We begin by introducing the Prior Day Weather Sensitivity (PDWS), an indicator that quantifies the impact of prior day temperature on daily gas demand. By investigating the complex relationship between prior day temperature and daily gas demand, we derived a PDWS function that suggests PDWS varies by temperature and temperature changes. We show that by accounting for this PDWS function in a gas demand model, we obtain a gas model with better predictive power. We present results that show improved accuracy for most unusual day types

Blending as a Multi-Horizon Time Series Forecasting Tool

Every day, millions of cubic feet of natural gas is transported through interstate pipelines and consumed by customers all over the United States of America. Gas distributors, responsible for sending natural gas to individual customers, are eager for an estimate of how much natural gas will be used in the near future. GasHour software, a reliable forecasting tool from the Marquette University GasDay lab, has been providing highly accurate hourly forecasts over the past few years. Our goal is to improve current GasHour forecasts, and my thesis presents an approach to achieve that using a blending technique. This thesis includes detailed explanations of the multi-horizon forecasting technique employed by GasHour models. Several graphs are displayed to reveal the structure of hourly forecasts from GasHour. We present SMHF (Smoothing Multi-horizon Forecasts), a step-by-step method showing how a polynomial smoothing technique is applied to current GasHour predications. A slightly different approach of smoothing has also been introduced. We compare RMSEs of both GasHour forecasts and smoothed ones. Different comparisons resulting from different situations have been demonstrated as well. Several conclusions have been reached. Based on the results, blending techniques can improve current GasHour forecasts. We look forward to applying this technique of blending to other fields of forecasting