Prediction Of Gas Consumption Research Articles

Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network

With increasing consumption of primary energy and deterioration of the global environment, clean energy sources with large reserves, such as natural gas, have gradually gained a higher proportion of the global energy consumption structure. Monitoring and predicting consumption data play a crucial role in reducing energy waste and improving energy supply efficiency. However, owing to factors such as high monitoring device costs, safety risks associated with device installation, and low efficiency of manual meter reading, monitoring natural gas consumption data at the household level is challenging. Moreover, there is a lack of methods for predicting natural gas consumption at the household level in residential areas, which hinders the provision of accurate services to households and gas companies. Therefore, this study proposes a gas consumption monitoring method based on the K-nearest neighbours (KNN) algorithm. Using households in a residential area in Xi’an as research subjects, the feasibility of this monitoring method was validated, achieving a model recognition accuracy of 100%, indicating the applicability of the KNN algorithm for monitoring natural gas consumption data. In addition, this study proposes a framework for a natural gas consumption prediction system based on a backpropagation (BP) neural network.

A Hybrid Model Based on Grey Wolf Optimizer and Lagrangian Support Vector Regression for European Natural Gas Consumption Forecasting

Natural gas plays an important role in industry as a clean energy, with the intensification of the Russia-Ukraine war, there is a large-scale energy shortage in Europe, and the natural gas supply in Europe has a natural gas crisis due to the cut-off of the Nord Stream No.1 pipeline. Therefore, it is necessary to accurately predict the consumption of natural gas. In order to fulfill this requirement, this paper uses the Lagrangian Support Vector Regression model with Sorensen kernel based on the Nonlinear Auto-Regressive model and Grey Wolf Optimizer for 5-step forecasting of monthly natural gas consumption in all European countries. Under three time lags, comparing the 5-step predict results of GWO-LSVR with SVR, RF, LightGBM, XGBoost, and MLP, those five models’ hyperparameters also optimized by GWO, it found that GWO-LSVR has smallest MAPE in almost all cases, and the numerical results of MAPE generated by GWO-LSVR is from 5.844% to 11.622%, the smaller the forecasting step size, the better the effect. Moreover, compares the difference of GWO and WOA, it is found that GWO can obtained better model hyperparameters and smaller MAPE results. To sum up, the proposed GWO-LSVR model has strong generalization performance and robustness, and is a reliable natural gas consumption prediction model.

Prediction of Food Factory Energy Consumption Using MLP and SVR Algorithms

The industrial sector accounts for a significant proportion of total energy consumption. Factory Energy Management Systems (FEMSs) can be a measure to reduce energy consumption in the industrial sector. Therefore, machine learning (ML)-based electricity and liquefied natural gas (LNG) consumption prediction models were developed using data from a food factory. By applying these models to FEMSs, energy consumption can be reduced in the industrial sector. In this study, the multilayer perceptron (MLP) algorithm was used for the artificial neural network (ANN), while linear, radial basis function networks and polynomial kernels were used for support vector regression (SVR). Variables were selected through correlation analysis with electricity and LNG consumption data. The coefficient of variation of root mean square error (CvRMSE) and coefficient of determination (R2) were examined to verify the prediction performance of the implemented models and validated using the criteria of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers Guideline 14. The MLP model exhibited the highest prediction accuracy for electricity consumption (CvRMSE: 17.35% and R2: 0.84) and LNG consumption (CvRMSE: 12.52% and R2: 0.88). Our findings demonstrate it is possible to attain accurate predictions of electricity and LNG consumption in food factories using relatively simple data.

Prediction of gas consumption based on LSTM-BPNN hybrid model

ABSTRACT The economic benefits of iron and steel enterprises require the support of Byproduct gas prediction. A novel hybrid model for data pre-processing is proposed to ensure the stability and accuracy of gas prediction. First, each component obtained from Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposition is processed using Sample Entropy (SE), and the members are denoised and restructured by singular spectrum analysis (SSA). Then, the restructured data are predicted by the Back Propagation Neural Network (BPNN) and Long Short-Term Memory (LSTM) and weighted by Particle Swarm Optimization (PSO). Finally, the data are integrated to obtain the final results. The novel hybrid model makes allowances for the linear and nonlinear characteristics of the series and successfully overcomes the limitations of individual models to get accurate and stable prediction results. The results show that the prediction accuracy is improved by 20% on average by adopting the novel hybrid model.

Data-driven modelling for gas consumption prediction at City Gate Stations

City Gate Stations (CGS) are critical elements of Natural Gas (NG) distribution systems, as they connect national high-pressure transmission networks with local low-pressure networks. One of their main tasks is to pre-heat the gas to avoid dangerous sub-cooling due to the Joule-Thompson effect after the pressure reduction stage. For this process, significant amounts of thermal energy are required, usually obtained by burning part of the gas flow rate. This work aims to develop a data-driven model that will serve as a tool to predict and monitor the thermal consumption of the CGS. The plant chosen as a case study for this activity is in a region of central Italy. A Multiple Linear Regression (MLR) model is developed and trained, and its predictive performance is evaluated. The model results achieved an accuracy of over 95% for the coefficient of determination. The method makes it possible to create a baseline consumption model and evaluate the performance of the CGS over time using the CUSUM technique to find variations between actual and modelled gas consumption, being an essential tool for monitoring the effectiveness of the NG preheating system.

A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction

The accurate prediction of natural gas consumption plays a central role in long-distance pipeline system production and transportation planning, and it becomes even more important during present political situation. The existing prediction methods for natural gas consumption barely consider spatial correlations and domain knowledge. As a result, the study proposes a novel deep learning prediction method (knowledge-enhanced graph-based temporal-spatial network, abbreviated to KE-GB-TSN) for predicting natural gas consumption by integrating domain knowledge into association graph construction and capturing temporal-spatial features via a hybrid deep learning network. This study first applies the domain knowledge that analyses the operation technique of the natural gas pipeline network and combines the historical data to establish an association graph. Subsequently, the historical data and association graphs are input to a hybrid deep learning network to predict natural gas consumption. The comparative experiments are conducted by taking real-world cases of natural gas consumption as examples. At last, a sensitivity analysis of different components combination is carried out to exhibit the significance of each component in the proposed model. The results prove that the proposed model is capable of achieving more accurate and efficient predicted results compared to the advanced models, such as decision trees and gated recurrent units. The Mean Absolute Relative Errors and Root Mean Squared Relative Errors gotten by the proposed model are less than 0.11 and 0.14 in all cases, indicating an improvement compared to previous works. Additionally, it is also suggested that domain knowledge and temporal-spatial correlations are crucial for the excellent performance of the prediction model.

A hybrid deep learning framework for predicting daily natural gas consumption

Conventional time-series prediction methods for natural gas consumption mainly focus on capturing the temporal feature, neglecting static and dynamic information extraction. The accurate prediction of natural gas consumption possesses of paramount significance in the normal operation of the national economy. This paper proposes a novel method that resolves the deficiency of conventional time series prediction to address this demand via designing a hybrid deep learning framework to extract comprehensive information from gas consumption. The proposed model captures static and dynamic information via encoding gas consumption as matrices and extracts long-term dependency patterns from time series consumption. Subsequently, a customised network is proposed for information fusion. Cases from several different regions in China are studied as examples, and the proposed model is compared with other advanced approaches (such as long short-term memory (LSTM), convolution neural network long short-term memory (CNN-LSTM)). The mean absolute percentage error is reduced by a range of 0.235%–10.303% compared with other models. According to the comparison results, the proposed model provides an efficient time series prediction functionality. It is also proved that, after effectively extracting comprehensive information and integrating long-term information with static and dynamic information, the accuracy and efficiency of natural gas consumption prediction are greatly promoted. A sensitivity analysis of different modules combination is conducted to emphasise the significance of each module in the hybrid framework. The results indicate that the method coupling all these modules leads to significant improvement in prediction accuracy and robustness.

Short‐term natural gas consumption prediction based on wavelet transform and bidirectional long short‐term memory optimized by Bayesian network

AbstractIn the energy structure, natural gas is an important clean energy, and its consumption forecast is of great significance for energy policy formulation, pipeline network planning design, and peak shaving capacity determination. In the field of forecasting, wavelet transform (WT) is widely used to process time series, especially natural gas consumption. However, in the application of the WT, the wavelet's orders and levels are randomly fixed. To solve this problem, this study proposes a hybrid model, which is based on bidirectional long short‐term memory (BiLSTM). In addition, Bayesian networks are used to optimize the hyperparameters of BiLSTM. And the different wavelets' orders and levels are utilized to deal with the daily natural gas consumption (DNLGSCN). When each level of Coiflets wavelets' four and five orders are used to process the DNLGSCN, the fifth level of Coiflets wavelets' four and five orders have the highest prediction accuracy, respectively, which is 0.3318 and 0.1773, taking U1 as an example. Using Symflets wavelet to decompose the DNLGSCN also gives the same result. Compared with the decomposition method (e.g., ensemble empirical mode decomposition), the prediction performance of the above four wavelet decomposition methods is better. The main conclusions are that decomposing the DNLGSCN with the fifth level of Coiflets wavelet's five orders has the best prediction performance.

Prediction of cold rolling gas based on EEMD-LSTM deep learning technology

PurposeThe prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a basis for gas dispatch and reduce the production cost of enterprises.Design/methodology/approachIn this paper, a new method using the ensemble empirical mode decomposition (EEMD) and the back propagation neural network is proposed. Unfortunately, this method does not achieve the ideal prediction. Further, using the advantages of long short-term memory (LSTM) neural network for long-term dependence, a prediction method based on EEMD and LSTM is proposed. In this model, the gas consumption series is decomposed into several intrinsic mode functions and a residual term (r(t)) by EEMD. Second, each component is predicted by LSTM. The predicted values of all components are added together to get the final prediction result.FindingsThe results show that the root mean square error is reduced to 0.35%, the average absolute error is reduced to 1.852 and the R-squared is reached to 0.963.Originality/valueA new gas consumption prediction method is proposed in this paper. The production data collected in the actual production process is non-linear, unstable and contains a lot of noise. But the EEMD method has the unique superiority in the analysis data aspect and may solve these questions well. The prediction of gas consumption is the result of long-term training and needs a lot of prior knowledge. Relying on LSTM can solve the problem of long-term dependence.

Integration of weather forecast and artificial intelligence for a short-term city-scale natural gas consumption prediction

Ruled by the directives and regulations adopted by the European Commission, the European natural gas market is obliged to enable free competition. This process has just started in the Serbian market, and the national natural gas provider (Srbijagas) has started its restructuring. To attain the objective of natural gas market liberalization, it is essential to accurately plan capacities and make precise predictions of future peak consumption at all levels of trade. In this research, an hourly city-scale Adaptive Neuro-Fuzzy Inference System algorithm with the Gaussian membership function is developed for both heating and non-heating regimes. In order to upgrade previous studies in this field, besides standard gas consumption and weather variables, hourly weather forecast and lower calorific value were added to the model. The model output is predicted hour-ahead natural gas consumption. To measure the predicting performance of the two models proposed in this research, the Coefficient of Determination and Mean Absolute Percentage Error were used. For the considered hourly consumption forecast of heating and non-heating models, the Coefficients of Determination were 0.99, while the overall (training, testing and checking) Mean Absolute Percentage Error values were 3.0% and 3.4% respectively. Based on these performance metrics, it can be concluded that for both models, the proposed algorithm resulted in a very satisfactory forecast. An adequate prediction of future natural gas consumption would support all market stakeholders, leading to the development a cleaner and sustainable energy system.

Nature-inspired hybrid techniques of IWO, DA, ES, GA, and ICA, validated through a k-fold validation process predicting monthly natural gas consumption

Current study aimed to combine the multi-layer Perceptron (MLP) neural network technique with five metaheuristic computational algorithms, namely invasive weed optimization (IWO-MLP), dragonfly algorithm (DA-MLP), evolution strategy (ES-MLP), genetic algorithm (GA-MLP), and imperialist competitive algorithm (ICA-MLP) for estimating the monthly natural gas consumption (NGC). For the case of this study, the NGC data was collected from the United States (U.S.) was chosen. To achieve this, a dataset composed of eight independent factors, namely, dry gas production, NGPL production, gross withdrawals, supplemental gaseous fuels, net storage withdrawals, marketed production, net imports, and balancing items, along with a dependent variable of natural gas consumption is provided. Out of 39 samples, a 3-fold ratio is taken to select the datasets of training and testing randomly. During the calculation phase, and through a trial and error procedure, the optimal parameters of the MLP, IWO-MLP, DA-MLP, ES-MLP, GA-MLP, ICA-MLP networks are taken into consideration. To assess the reliability of the proposed technique with the real collected data, three well-known statistical criteria, including mean square error (MSE) and root mean square error (RMSE) as well as coefficient of determination (R²), are introduced. These indices help to measure the level of accuracy of the used models. It is found that the IWO can be a better predictive network comparing to other hybridized techniques reducing the error performance of the MLP method. The predicted results reveal that hybridizing optimization algorithms could improve the prediction accuracy and helps the MLP to perform more efficiently. The recorded in testing MSE for the IWO-MLP, DA-MLP, ES-MLP, GA-MLP, ICA-MLP prediction techniques (i.e., used for U.S. natural gas consumption estimation) were found 0.000013, 0.002358, 0.024744, 0.013244, and 0.048572, respectively. In the sense of U.S. natural gas consumption prediction, the training R2 value to 0.9999, 0.9996, 0.9971, 0.9996, and 1.0000 and testing R2 value of 0.9999, 0.9995, 0.9956, 0.9984, and 0.8115 show that the outputs of the ensemble models are more correlated with the real data.

Modeling Method of the Grey GM(1,1) Model with Interval Grey Action Quantity and Its Application

GM(1,1) is a univariate grey prediction model with incomplete structural information, in which the real number form of the simulation or prediction data does not conform to the Nonuniqueness Principle of Grey theoretical solution. In light of the network model of GM(1,1), the connotation of grey action quantity is systematically analyzed and the interval grey number form of grey action quantity is restored under uncertain influencing factors. A novel GM(1,1) model is then constructed. The new model has the basic characteristics of the grey model under incomplete information. Moreover, it can be fully compatible with the traditional GM(1,1) model. The developed model is employed to the natural gas consumption prediction in China, showing that its predicting rationality is much better than that of the traditional GM(1,1) model. It is worth mentioning that, for the first time, the grey property of GM(1,1) has been restored in structure, which is of significance for both academia and industry.

US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model

Natural gas (NG) is a vital energy in the energy structure transition, and its consumption prediction is a significant issue in energy structure management and energy security. As the second largest energy consumer and producer in the world, the status of NG in the United States (US) energy system has been increasing since the “An America First Energy Plan” was proposed in 2017. Accurate prediction of natural gas consumption (NGC) can provide an effective reference for decision-makers, policymakers, and energy companies. This paper proposes an improved kernel-based nonlinear extension of the Arps decline model (KNEA) to forecast NGC in the US. The grey wolf optimization (GWO) algorithm is used to optimize the regularization parameter and kernel width in the KNEA model, and applies the hybrid model to the NGC datasets of different sectors (including lease and plant fuel usage, pipeline and distribution usage, residential users, commercial users, industrial users, vehicle fuels users, and power generation users) in the US. Compared with the prediction results of five benchmark models, it is shown that the GWO-KNEA model has the best performance in each dataset, and the range of mean absolute percentage error is less than 5%. By comparing the computational time and memory occupancy of the model, it can be concluded that the time and space complexity of the GWO-KNEA model is greater than that of the original KNEA model, but lower than that of other benchmark models. Moreover, this paper uses the newly proposed model to predict the NGC and consumption mix of the US from 2019 to 2025. The main conclusions are drawn: (1) NGC in the US will show a slow growth trend (the average annual growth rate is only 1.2%); (2) The proportion of NGC in power generation will increase significantly, reaching about 39% in 2025; (3) The proportion of residential, commercial and industrial NGC will decline slightly.

Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm

Short-term natural gas consumption prediction is an important indicator of natural gas pipeline network planning and design, which is of great significance. The purpose of this study is to propose a novel hybrid forecast model in view of the Volterra adaptive filter and an improved whale optimization algorithm to predict the short-term natural gas consumption. Firstly, Gauss smoothing and C–C method is adopted to pretreat and reconstruct short-term natural gas consumption time series; secondly, to improve the performance of whale optimization algorithm, adaptive search-surround mechanism and spiral position and jumping behavior are introduced into it; Thirdly, Volterra adaptive filter is used to predict the short-term natural gas consumption, and the important parameters (e.g. embedding dimension) is optimized by improved whale optimization algorithm. Finally, an actual example is given to test the performance of the developed prediction model. The results indicate that (1) short-term natural gas consumption time series has chaotic characteristics; (2) performance of the improved whale optimization algorithm is better than some comparative algorithms (i.e. cuckoo optimization algorithm, etc. ) based on the different evaluation indicators; (3) exploration factor is the main operational factor; (4) the performance of the proposed prediction model is better than some advanced prediction models (e.g. back propagation neural network). It can be concluded that such an innovative hybrid prediction model may provide a reference for natural gas companies to achieve intelligent scheduling.

Data-driven approach to prediction of residential energy consumption at urban scales in London

Development of energy prediction models plays an integral part in management and enhancement of the energy efficiency of buildings, including carbon emission reduction. Simplified and data-driven models are often the preferred option when detailed information of simulation is not available and the fast responses are required. This study developed data-driven models for predicting electricity and gas consumption in London’s residential buildings at the middle super output areas (MSOA) and lower super output areas (LSOA) with multilayer neural network (MNN), multiple regression (MLR), random forest (RF), and gradient boosting (GB) algorithms, and factors related to socio-demographic, economic, and building characteristics were used as predictors. The results revealed that building characteristics, household income, and the number of households were the most important predictors of electricity and gas consumption. We also found that MNN models have outperformed MLR, RF and GB models in electricity and gas consumption prediction at MSOA and LSOA levels, with R2 values over 0.99 for the electricity consumption model. In summary, this study shows that the MNN models can be a useful tool to assist the formation of energy efficiency policies in buildings at MSOA and LSOA levels.

Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks

Finding suitable forecasting methods for an effective management of energy resources is of paramount importance for improving the efficiency in energy consumption and decreasing its impact on the environment. Natural gas is one of the main sources of electrical energy in Algeria and worldwide. To address this demand, this paper introduces a novel hybrid forecasting approach that resolves the two-stage method's deficiency, by designing a Multi Layered Perceptron (MLP) neural network as a nonlinear forecasting monitor. This model estimates the next day gas consumption profile and selects one of several local models to perform the forecast. The study focuses firstly on an analysis and clustering of natural gas daily consumption profiles, and secondly on building a comprehensive Long Short Term Memory (LSTM) recurrent models according to load behavior. The results are compared with four benchmark approaches: the MLP neural network approach, LSTM, seasonal time series with exogenous variables models and multiple linear regression models. Compared with these alternative approaches and their high dependence on historical loads, the proposed approach presents a new efficient functionality. It estimates the next day consumption profile, which leads to a significant improvement of the forecasting accuracy, especially for days with exceptional customers consumption behavior change.

Prediction of Natural Gas Consumption for City-Level DHS Based on Attention GRU: A Case Study for a Northern Chinese City

With the increasing energy demand and environmental protection requirements of heating systems, natural gas (NG), as an efficient and clean alternative energy source for coal, plays an increasingly important role in district heating systems (DHSs). The accurate prediction of the DHS consumption of NG in the whole city is helpful in formulating an efficient energy scheduling plan. However, because the DHS is a complex non-linear system with time-space delay effect and multi-factors, the traditional algorithms have deficiencies in the prediction accuracy of the natural gas consumption for city-level DHS. To achieve accurate prediction, a gas consumption prediction algorithm based on the attention gated recurrent unit (AGRU) model is proposed, which can obtain high-level features of historical data that affect gas consumption prediction. In addition, the attention mechanism can help to select more critical feature inputs and improve the prediction accuracy of gas consumption. Detailed comparative experiments were performed between the proposed AGRU and state-of-art NG consumption prediction algorithms, such as the recurrent neural network (RNN), long short-term memory (LSTM), GRU, attention RNN (ARNN), attention LSTM (ALSTM), support vector regression (SVR), random forest regression (RFR), gradient boosting regression (GBR) and decision tree regression (DTR). An analysis of the experimental results shows that the proposed AGRU algorithm has a prediction accuracy of 95.3%, which is significantly better than other algorithms. In addition, the hyperparameters of the AGRU algorithm are also tested in detail to optimize the selection.

Natural gas consumption prediction in Slovenian industry – a case study

Abstract In accordance with the regulations of the Energy Agency of the Republic of Slovenia, each natural gas supplier regulates and determines the charges for the differences between the ordered (predicted) and the actually supplied quantities of natural gas. Yearly charges for these differences represent up to 2% of supplied natural gas costs. All the natural gas users, especially industry, have huge problems finding the proper method for efficient natural gas consumption prediction and, consequently, the decreasing of mentioned costs. In this study, prediction of the natural gas consumption in Štore Steel Ltd. (steel plant) is presented. On the basis of production data, several models for natural gas consumption have been developed using linear regression, genetic programming and artificial neural network methods. The genetic programming approach outperformed linear regression and artificial neural networks.

Predicting Operational Energy Consumption Profiles - Findings from Detailed Surveys and Modelling in a UK Educational Building Compared to Measured Consumption

This paper presents the preliminary findings from the first stage of a physical survey and modelling Case Study conducted to obtain Modelled and Actual Energy Consumption Profiles for a UK multi-storey mixed use educational building. The purpose of the study is to provide an insight into how accurately current models and software can predict the actual energy consumption in such a building, with a view to informing the development of Operational and Asset Ratings for Buildings in the EU as part of the EPBD Article 7 requirements. The study also briefly discusses the potential problems inherent in the use of modelling techniques for assessing the energy performance of buildings. The data obtained through this study enabled predicted energy consumption profiles for both heating/cooling and electrical energy use to be obtained, as well as a UK SBEM asset-type compliance rating. The predicted profiles and compliance rating were then compared to the monitored actual energy consumption profiles obtained over the same period. It was seen that the various modelling approaches gave a reasonable prediction of the gas consumption, and a reasonable estimate of the electrical consumption using the SBEM. However, overall it was felt that the models were still too inaccurate to be used with any confidence. The relative agreement between the SBEM results in this Case Study and the measured consumption supports the view that for prediction of electrical consumption then statistically derived numbers, such as benchmarks, are likely to enable reasonably confident predictions of energy use by generic activity type.

Prediction of the natural gas consumption in chemical processing facilities with genetic programming

Cinkarna Ltd. is a chemical processing company in Slovenia and the country's largest manufacturer of titanium oxides (TiO2). Chemical processing and titanium oxide manufacturing in particular requires high natural gas consumption, and it is difficult to accurately pre-order gas from suppliers. In accordance with the Energy Agency of the Republic of Slovenia regulations, each natural gas supplier regulates and determines the charges for the differences between the ordered (predicted) and the actually supplied quantities of natural gas. Yearly charges for these differences total 1.11 % of supplied natural gas costs (average 50,960 EUR per year). This paper presents natural gas consumption prediction and the minimization of associated costs. The data on daily temperature, steam boilers, sulfur acid and TiO2 production was collected from January 2012 until November 2014. Based on the collected data, a linear regression and a genetic programming model were developed. Compared to the specialist's prediction of natural gas consumption, the linear regression and genetic programming models reduce the charges for the differences between the ordered and the actually supplied quantities by 3.00 and 5.30 times, respectively. Also, from January until November 2014 the same genetic programming model was used in practice. The results show that in a similar gas consumption regime the differences between the ordered and the actually supplied quantities are statistically significant, namely, they are 3.19 times lower (t test, p < 0.05) than in the period in which the specialist responsible for natural gas consumption made the predictions.