Gas Demand Forecasting Research Articles

Automatic specification of piecewise linear additive models: application to forecasting natural gas demand

When facing any forecasting problem not only is accuracy on the predictions sought. Also, useful information about the underlying physics of the process and about the relevance of the forecasting variables is very much appreciated. In this paper, it is presented an automatic specification procedure for models that are based on additivity assumptions and piecewise linear regression. This procedure allows the analyst to gain insight about the problem by examining the automatically selected model, thus easily checking the validity of the forecast. Monte Carlo simulations have been run to ensure that the model selection procedure behaves correctly under weakly dependent data. Moreover, comparison over other well-known methodologies has been done to evaluate its accuracy performance, both in simulated data and in the context of short-term natural gas demand forecasting. Empirical results show that the accuracy of the proposed model is competitive against more complex methods such as neural networks.

Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model

Accurate forecasts of natural gas demand can be essential for utilities, energy traders, regulatory authorities, decision makers and others. The aim of this paper is to test the robustness of a novel hybrid computational intelligence model in day-ahead natural gas demand predictions. The proposed model combines the Wavelet Transform (WT), Genetic Algorithm (GA), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Feed-Forward Neural Network (FFNN). The WT is used to decompose the original signal in a set of subseries and then a GA optimized ANFIS is employed to provide the forecast for each subseries. ANFIS output is fed into a FFNN to refine the initial forecast and upgrade the overall forecasting accuracy. The model is applied to all distribution points that compose the natural gas grid of a country, in contradiction to the majority of the literature that focuses on a limited number of distribution points. This approach enables the comparison of the model performance on different consumption patterns, providing also insights on the characteristics of large urban centers, small towns, industrial areas, power generation units, public transport filling stations and others.

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

Consumption of natural gas, a major clean energy source, increases as energy demand increases. We studied specifically the Turkish natural gas market. Turkey’s natural gas consumption increased as well in parallel with the world‘s over the last decade. This consumption growth in Turkey has led to the formation of a market structure for the natural gas industry. This significant increase requires additional investments since a rise in consumption capacity is expected. One of the reasons for the consumption increase is the user-based natural gas consumption influence. This effect yields imbalances in demand forecasts and if the error rates are out of bounds, penalties may occur. In this paper, three univariate statistical methods, which have not been previously investigated for mid-term year-ahead monthly natural gas forecasting, are used to forecast natural gas demand in Turkey’s Sakarya province. Residential and low-consumption commercial data is used, which may contain seasonality. The goal of this paper is minimizing more or less gas tractions on mid-term consumption while improving the accuracy of demand forecasting. In forecasting models, seasonality and single variable impacts reinforce forecasts. This paper studies time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA) methods. Here, 2011–2014 monthly data were prepared and divided into two series. The first series is 2011–2013 monthly data used for finding seasonal effects and model requirements. The second series is 2014 monthly data used for forecasting. For the ARIMA method, a stationary series was prepared and transformation process prior to forecasting was done. Forecasting results confirmed that as the computation complexity of the model increases, forecasting accuracy increases with lower error rates. Also, forecasting errors and the coefficients of determination values give more consistent results. Consequently, when there is only consumption data in hand, all methods provide satisfying results and the differences between each method is very low. If a statistical software tool is not used, time series decomposition, the most primitive method, or Winters exponential smoothing requiring little mathematical knowledge for natural gas demand forecasting can be used with spreadsheet software. A statistical software tool containing ARIMA will obtain the best results.

Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption

DHS (District Heating System) is one of the most efficient technologies which has been used to meet residential thermal demand. In this study, the most accurate forecasting of the residential heating demand is investigated via soft computing method. The objective of this study is to obtain the most accurate prediction of the residential heating consumption to employ forecasting result for designing optimum DHS system as a possible substitute of a pipeline natural gas in BAHARESTAN Town. For this purpose, three Support Vector Machine (SVM) models namely SVM coupled with the discrete wavelet transform (SVM-Wavelet), the firefly algorithm (SVM-FFA) and using the radial basis function (SVM-RBF) were analyzed. The estimation and prediction results of these models were compared with two other soft computing methods (ANN (Artificial Neural Network) and GP (Genetic programming)) by using three statistical indicators i.e. RMSE (root means square error), coefficient of determination (R2) and Pearson coefficient (r). Based on the experimental outputs, the SVM-Wavelet method can lead to slightly accurate forecasting of the monthly overall natural gas demand.

Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia

In this paper the performance of static and adaptive models for short-term natural gas load forecasting has been investigated. The study is based on two sets of data, i.e. natural gas consumption data for an individual model house, and natural gas consumption data for a local distribution company. Various forecasting models including linear models, neural network models, and support vector regression models, were constructed for the one day ahead forecasting of natural gas demand. The models were examined in their static versions, and in adaptive versions. A cross-validation approach was applied in order to estimate the generalization performance of the examined forecasting models. Compared to the static model performance, the results confirmed the significantly improved forecasting performance of adaptive models in the case of the local distribution company, whereas, as was expected, the forecasts made in the case of the individual house were not improved by the adaptive models, due to the stationary regime of the latter’s heating. The results also revealed that nonlinear models do not outperform linear models in terms of generalization performance. In summary, if the relevant inputs are properly selected, adaptive linear models are recommended for applications in daily natural gas consumption forecasting.

Combination Regression and Neural Network for Short Term Load Forecasting

The forecasting of gas demand has become one of the major research fields in gas engineering. Gas demand possesses dual property of increasement and seasonal fluctuation simultaneously, so it makes gas demand variation possess complex nonlinear combined character. Accurately forecast were essential part of an efficient gas system planning and operation. In this paper, a new forecasting model which named regression combined neural network is put forward. In this approach we used regression to model the trend and used neural network for calculating predicted values and errors. Taking the advantages of regression analysis and artificial neural network, the model improves the forecasting accuracy of power demand obviously. The results indicate that the model is effective and feasible for load forecasting.

An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments

Accurate short-term natural gas (NG) demand estimation and forecasting is vital for policy and decision-making process in energy sector. Moreover, conventional methods may not provide accurate results. This paper presents an adaptive network-based fuzzy inference system (ANFIS) for estimation of NG demand. Standard input variables are used which are day of the week, demand of the same day in previous year, demand of a day before and demand of 2 days before. The proposed ANFIS approach is equipped with pre-processing and post-processing concepts. Moreover, input data are pre-processed (scaled) and finally output data are post-processed (returned to its original scale). The superiority and applicability of the ANFIS approach is shown for Iranian NG consumption from 22/12/2007 to 30/6/2008. Results show that ANFIS provides more accurate results than artificial neural network (ANN) and conventional time series approach. The results of this study provide policy makers with an appropriate tool to make more accurate predictions on future short-term NG demand. This is because the proposed approach is capable of handling non-linearity, complexity as well as uncertainty that may exist in actual data sets due to erratic responses and measurement errors.

North American Natural Gas Demand — Outlook 2020

AbstractThis paper analyses natural gas demand in the United States of America, Canada and Mexico for the period 1980–2001. Visual inspections of the historical trends for natural gas demand, GDP and natural gas prices provide an initial assessment of the relationship between these variables.The paper then estimates various regressions, using the Almon polynomial distributed lag model. Several regressions, utilizing different combinations of explanatory variables, were run with or without an autoregressive scheme of various orders. The best‐fitting model in each country was selected, and this was later used to generate natural gas demand forecasts for the respective countries to 2020.The results show that the income elasticity for Canada is more than unity (1.4), compared with less then unity for the USA (0.8) and Mexico (0.65). Changes in natural gas consumption generally lag by a few years, before the full impact of the changes in gas prices is realised. The length of lags varies between countries, but generally the effect of a price change on natural gas consumption dissipates after three‐to‐four years. Population is one of the important explanatory variables for Mexico, while it is not significant for the USA and Canada.The model predicts that natural gas demand in the USA (base case) will increase at an annualised growth rate of 1.9 per cent and, by 2020, should reach 919 billion cubic metres. This is not very different to the forecast of the US Department of Energy's Energy Information Administration (EIA) (900 bcm). Similarly, the base case forecast for Canada to 2020 is 128 bcm, compared with that of the EIA at 125 bcm. The forecast for Mexico (92 bcm) is significantly different to that of the EIA (120), perhaps due to different economic growth assumptions.The paper concludes that there is an urgent need to enhance exploration efforts and investment in liquefied natural gas (LNG) terminals, to be able to receive LNG from multiple sources, particularly in the USA and Mexico.

Artificial neural networks for short-term energy forecasting: Accuracy and economic value

Sixteen electric utilities surveyed state that use of ANNs significantly reduced errors in daily electric load forecasts, while only three found otherwise. Data for five gas utilities reinforces this result: the mean absolute percentage error (MAPE) for ANN daily gas demand forecasts was 6.4%, a 1.9% improvement over previous methods. Yet ANNs were not always best, implying opportunities for further improvement. The economic value of error reduction for electric utilities was assessed by examining operating decisions. For 19 utilities surveyed, an average of $800 000/year per utility is estimated to be saved from use of ANN-based forecasts. Most benefits resulted from improved generating unit scheduling; the utilities estimated such benefits to be up to $143 annually per peak MW of demand for each 1% improvement in MAPE. This estimated worth of accuracy improvement (roughly 0.1% of annual generation O&M costs) is confirmed by solving generation scheduling and dispatch models under various levels of forecast accuracy.

Nigeria: The Liquefied Natural Gas Project

After a few false dawns, the Nigerian Liquefied Natural Gas Project is today moving from concept to reality having overcome the preliminary steps of gas reserve estimation, preliminary engineering and demand forecasting. This article provides a concise description of the project structure detailing the essential integrated elements of the chain, the sustenance of the project and the defined commercial arrangements for its finance and marketing. It also takes a look at the potential benefits the success of the project holds for Nigeria.

5473900 Method and apparatus for liquefaction of natural gas

After a few false dawns, the Nigerian Liquefied Natural Gas Project is today moving from concept to reality having overcome the preliminary steps of gas reserve estimation, preliminary engineering and demand forecasting. This article provides a concise description of the project structure detailing the essential integrated elements of the chain, the sustenance of the project and the defined commercial arrangements for its finance and marketing. It also takes a look at the potential benefits the success of the project holds for Nigeria.

Antithetic variate methods for simulations of processes with peaks and troughs

To save costs in computer simulations, the well-known method of antithetic variates is often suggested for reducing the variability of estimated quantities. The method can be a dangerous one to use however, as, if incorrectly applied, it can be useless or worse than useless. Recent studies have shown how the method can be used successfully, but these have concentrated mainly on steady state systems and the quantities of interest in such processes. In this paper we consider problems where maxima and minima are of interest and suggest simple antithetic strategies which can be applied in such situations. The methods have the advantage of not being particularly problem dependent so that they can be applied in a fairly automatic manner by the call to set routines at certain points in a simulation. The methods are illustrated by two practical case studies. One involves the forecasting of peak gas demand and the other involves the study of the drought and flooding characteristics of a river system. In both examples the computing costs are high. It is shown that the suggested methods are easily applied and lead to significant savings in cost.

Consistent Forecasting of Severe Winter Gas Demand

British Gas plans to be able to meet the demand for gas which might occur in a very cold winter such as might occur with a frequency of once in fifty years. Every year the twelve Regions of British Gas renew their forecasts of the 1 in 50 demand for the following five winters. The problem addressed by the paper has two aspects; first, how do we decide what constitutes 1 in 50 winter conditions?, and second, what will be the demand for gas in such conditions? The paper describes how the problem has evolved over the years, and the approach taken by a new Steering Group over the period January 1979-July 1980. This has included the development of a Monte-Carlo simulation model to generate daily gas demands through a winter period over a wide range of conditions. The result has been a new set of guidelines enabling a higher quality and consistency of the Industry's forecasts.

Consistent Forecasting of Severe Winter Gas Demand

Consistent Forecasting of Severe Winter Gas Demand

The Relation between Gas Demand and Temperature: A Study in Statistical Demand Forecasting

The paper describes the methods of relating gas demand to temperature which have previously been used for forecasting gas demand in the North Western Area, and introduces a new approach which involves the use of exponential weighting as applied to the average temperatures occurring in the 4 weeks prior to the forecast. An analysis of previous gas demand experience (in relation to actual temperatures throughout) showed that the new method gave a much improved fit to the data, and that this accuracy was maintained even in prolonged periods of abnormal weather conditions. The new method also threw more light on the effect on gas demand of other meteorological factors apart from temperature, and showed up significant effects which had not been detected when using other methods. The method can be used for forecasting on both a daily and a weekly basis, and is considered to represent a significant step forward in the technique of gas demand forecasting.