Natural gas consumption forecasting for anomaly detection

Baldacci, Lorenzo; Golfarelli, Matteo; Lombardi, Davide; Sami, Franco

doi:10.1016/j.eswa.2016.06.013

Cited by 42 publications

(23 citation statements)

References 23 publications

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“…Also, numerous notable studies have investigated demand prediction for the case of energy resources [24][25][26][27][28][29]. Among those, Baumeister and Kilian published a research paper to analyze how vector autoregression (VAR) models form policy-relevant forecasting scenarios in the case of an oil market.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Time series [24,25,31,[34][35][36][37][38][39][40][41][42][43] Regression [28,[44][45][46][47] Econometrics [48][49][50][51][52] Expert systems and learning models Artificial neural network (ANN) [21,40,[53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69] Genetic programming (GP) [21,24,40,58,65,67,[69][70][71][72]…”

Section: Classical Computational Extrapolationmentioning

confidence: 99%

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Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

et al. 2019

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Recently, the natural gas (NG) global market attracted much attention as it is cleaner than oil and, simultaneously in most regions, is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and pre-processing selected input vector. Then a hybrid artificial neural network (ANN) which is equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g., Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) were selected as the most relevant feature via the DM step. Then, the hybrid learning prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different five error based evaluation statistics consist of R2, MAE, MAPE, MBE, and RMSE. In addition, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process. The comparison showed that DmGNn overcame dramatically a simple GNn. Also, a looser prediction model, such as a generalized neural network with purified input features obtained a larger R2 indicator (=0.9864) than the GNn (=0.9679).

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Section: Literature Reviewmentioning

confidence: 99%

Section: Classical Computational Extrapolationmentioning

confidence: 99%

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

et al. 2019

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show abstract

“…Delic claimed that intelligent systems will play a significant role in a mid-term future (Delic 2006) (see figure 2). Time series (for instance (Sen, Roy, and Pal 2016, Fagiani et al 2015, Shi, Liu, and Wei 2016, Zhang and Yang 2015, Taşpınar, Celebi, and Tutkun 2013, Ervural, Beyca, and Zaim 2016, Zhu et al 2015), regression (for instance (Dalfard et al 2013, Bianco, Scarpa, and Tagliafico 2014, Baldacci et al 2016) and econometrics (for instance (Adams and Shachmurove 2008, Ramanathan 2006, Iniyan, Suganthi, and Samuel 2006) were used widely to study future behaviors, which generally are developed for stock market predictions. Among all benefits traditional models have some crucial challenges, (1) focusing on historical data with a fundamental assumption which claims the future follows the past, (2) jumps and drips are not considered in the model and (3) they do not use root tests and feedbacks to estimate parameters.…”

Section: Review On Using Artificial Intelligence To Predict Future Trmentioning

confidence: 99%

How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments

Hafezi

2019

Preprint

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Decision-makers are concerned with the inherent complexity of the modern world's markets. However, price fluctuations, environmental concerns, technological development, emerging markets, political challenges, and social expectations made the 21st century's more dynamic and complex. From a policy-making perspective, it is vital to uncover future trends. This paper proposed that artificial intelligence can improve interpretations in complex markets, such as financial and energy markets. In a complex environment, it is critical to investigate maximum available input features to ensure no valuable informative feature is neglected. Some AI-based models are investigated and presented that AI-based models can successfully uncover future trends. From a scenario development perspective purified input features subset refer to driving forces which shape alternative futures. Results showed that using AI can improve our understanding of how input features influence future behaviors and simultaneously improves prediction accuracy and reliability.

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“…Some works address demand forecast exploiting weather, economic, and consumption data (Soldo 2012), especially to detect anomalies in gas usage through a nearest-neighbor algorithm and a local regression analysis (Baldacci et al 2016), to predict gas demand of residential and commercial users by training artificial neural networks with temperature and calendar data (Szoplik 2015), and to forecast the annual gas demand in Europe through a structural time-series model considering income, gas price, and energy demand trend (Dilaver et al 2014). As a major difference, these approaches use load prediction to support short-term monitoring of the network for security reasons and to optimize gas procurement in the medium-long term, while the proposed approach leverages load data to assess network performability and plan maintenance interventions.…”

Section: Related Workmentioning

confidence: 99%

Model-Based Quantitative Evaluation of Repair Procedures in Gas Distribution Networks

Biagi

Carnevali

Tarani

et al. 2018

ACM Trans. Cyber-Phys. Syst.

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We propose an approach for assessing the impact of multi-phased repair procedures on gas distribution networks, capturing load profiles that can depend on time for different classes of users, suspension of activities during non-working hours, and random execution times depending on topological, physical, and geographical characteristics of the network. The problem is characterized through a semi-formal specification based on artifacts of the Systems Modeling Language (SysML), which is then translated into a formal model based on stochastic time Petri nets. The solution method interleaves fluid-dynamic analysis of the gas behavior and stochastic analysis of the time spent in the repair process, decoupling complexities and making stochastic analysis almost insensitive to the network size and topology. Hence, our approach turns out to be applicable to real scale cases, notably computing the optimal time of day to start the repair procedure. Moreover, by encompassing general (non-Markovian) distributions, the approach enables effective fitting of durations.

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Natural gas consumption forecasting for anomaly detection

Cited by 42 publications

References 23 publications

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments

Model-Based Quantitative Evaluation of Repair Procedures in Gas Distribution Networks

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