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

Potočnik, Primož; Soldo, Božidar; Šimunović, Goran; Šarić, Tomislav; Jeromen, Andrej; Govekar, Edvard

doi:10.1016/j.apenergy.2014.04.102

Cited by 54 publications

(18 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a significant improvement that suggests that NN or the proposed NNLL structures are encouraged to be considered as forecasting solutions for applied forecasting in the district heating market. Further studies will be conducted to evaluate the adaptive versions of forecasting models [20], and take into account the influences of weather forecasting accuracy that influences heat load forecasts in online forecasting applications.…”

Section: Discussionmentioning

confidence: 99%

Linear and Neural Network-based Models for Short-Term Heat Load Forecasting

Potočnik¹,

Strmčnik²,

Govekar³

2015

SV-JME

Self Cite

View full text Add to dashboard Cite

Successful operation of a district heating system requires optimal scheduling of heating resources to satisfy heating demands. The optimal operation, therefore, requires accurate short-term forecasts of future heat load. In this paper, short-term forecasting of heat load in a district heating system of Ljubljana is presented. Heat load data and weather-related influential variables for five subsequent winter seasons of district heating operation are applied in this study. Various linear models and nonlinear neural network-based forecasting models are developed to forecast the future daily heat load with the forecasting horizon one day ahead. The models are evaluated based on generalization error, obtained on an independent test data set. Results demonstrate the importance of outdoor temperature as the most important influential variable. Other influential inputs include solar radiation and extracted features denoting population activities (such as day of the week). Comparison of forecasting models reveals good forecasting performance of a linear stepwise regression model (SR) that utilizes only a subset of the most relevant input variables. The operation of the SR model was improved by using neural network (NN) models, and also NN models with a direct linear link (NNLL). The latter showed the overall best forecasting performance, which suggests that NN or the proposed NNLL structures should be considered as forecasting solutions for applied forecasting in district heating markets.

show abstract

Section: Discussionmentioning

confidence: 99%

Linear and Neural Network-based Models for Short-Term Heat Load Forecasting

Potočnik¹,

Strmčnik²,

Govekar³

2015

SV-JME

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lin and Wang predicted the production peak and import trends of natural gas in China [8]. Primo et al compared static with adaptive models for shortterm residential natural gas forecasting in Croatia [9]. The Bayesian method of vector autoregression and neural network quantile regression are used separately to predict future natural gas consumption [10].…”

Section: Introductionmentioning

confidence: 99%

Forecasting natural gas production and consumption using grey model with latent information function: The cases of China and USA

Zhang

Zhao

2019

Scientia Iranica

View full text Add to dashboard Cite

The aim of the paper is to develop a grey model for short term forecasting of natural gas consumption and production in China and USA respectively. To enhance its prediction accuracy, the outliers are found by the error of the latent information function, and then corrected according to the test sample and the future trend. The sequence with corrected outliers is used to construct a grey model. The proposed model is employed to predict the natural gas consumption and production in China and USA. The results have demonstrated that the proposed model can raise the forecast accuracy of the grey model, and it also indicates that China will inevitably face a massive expansion in natural gas imports.

show abstract

“…In the second group, publications can be grouped as regional [8][9][10][11][12]15,18,19,[21][22][23][24] or national [6,7,13,14,20,[24][25][26][27][28][29][30][31] consumptions are investigated. In the third group, papers are investigated by consumer types.…”

Section: Related Workmentioning

confidence: 99%

“…This group includes household consumers [6][7][8][9][10][11][12], commercial consumers [11,13,25] and consumers where all consumption sectors are included [14][15][16][17][18][24][25][26][27][28][29][30][31]. In the fourth group, where studies are categorized by data used, papers are divided with respect to the use of only consumption data using univariate approaches [28][29][30][31] or independent variable [6][7][8][9][10][11][12][13][14][15][16][17][18]20,[22][23][24][25][26][27] included studies. Investigation of these studies showed that, mostly independent variable included regional-based natural gas consumption prediction is done.…”

Section: Related Workmentioning

confidence: 99%

“…In the first group, publications can be divided according to the use of timeframes which apply the time series method in daily periods [6][7][8][9][10][11][12][13][14][15][16][17][18][19] and monthly periods [20][21][22][23][24]. In the second group, publications can be grouped as regional [8][9][10][11][12]15,18,19,[21][22][23][24] or national [6,7,13,14,20,[24][25][26][27][28][29][30][31] consumptions are investigated.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Akpınar

Yumuşak

2016

Energies

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 54 publications

References 27 publications

Linear and Neural Network-based Models for Short-Term Heat Load Forecasting

Linear and Neural Network-based Models for Short-Term Heat Load Forecasting

Forecasting natural gas production and consumption using grey model with latent information function: The cases of China and USA

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

Contact Info

Product

Resources

About