# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
## 2 Related Work  
In the last decade, the number of publications regarding LNG supply chains have increased. [PERSON] et al. [5] presented a mixed-integer linear-programming (MILP) model for the optimal design of a supply chain for a problem where NG is distributed from a set of potential suppliers to a set of consumers using a heterogeneous fleet of ships. [PERSON] et al. [6], presented an MILP model for small-scale LNG supply chain optimization for supply along a coastline, minimizing the costs related to fuel procurement and providing optimal supply-chain configuration with regard to satellite-port locations, ship sizes, and customer demand. In the two cases presented above, LNG consumer demands must be known or accurately predicted. On the other hand, the prediction of natural gas consumption is indispensable for efficient system operation, and required for planning decisions at local distribution companies of natural gas. Researchers attempted to develop models for the prediction of NG consumption on a short-term (from 30 min to a week), medium-term (from a month to two years), and long-term (3-20 years) basis using mathematical and computational techniques. An extended overview of the published papers in the area of forecasting NG consumption can be found in [7; 8; 9; 10]. It is remarkable that the majority of prediction models were developed considering different types and numbers of input variables. [PERSON] et al. [11] used data-driven method multivariate adaptive regression splines (MARS) to determine the optimal combination of basic functions. Their proposed model included sixteen variables, among which maximal and medium temperature, price, and number of users. [PERSON] et al. [12] investigated the influence of solar radiation on improving NG consumption forecasting models. [PERSON] et al. [13] employed multiple linear regression (MLR), an artificial-neural-network approach (ANN), and support vector regression (SVR) for the forecasting of NG consumption in the province of Istanbul. The study indicated that SVR was far superior to the ANN technique, providing more reliable and accurate results in terms of lower prediction errors for time-series forecasting of natural gas consumption. The aforementioned methods use multivariate models that include natural gas consumption, meteorological data, seasonal trends, price of natural gas, population, among others, with an increase of data-collection difficulty. To solve this problem, the authors in [14] proposed a sensor-data-driven model based only on the time series of the NG consumption, presenting a structure-calibrated support-vector-regression (SC-SVR) approach for forecasting daily NG consumption that is correlated with past time series using the SVR model. The calibration of the structural parameters for the next-day forecast is performed by an extended Kalman filter. [PERSON] et al. [15] described the current state of the art and proposed an adaptive hybrid architecture based on a long short-term memory (LSTM) recurrent model for predicting efficiently the NG consumption, and a multilayered perceptron (MLP) neural network for estimating daily consumption. [PERSON] et al. [16] studied time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA) methods, the models proposed includes seasonal and trends components and are applied for the monthly demand prediction of NG. [PERSON] et al. [17] reviewed the contribution in the area of Fuzzy time series (FTS) forecasting, these models have proven to be a powerful tool for modelling and forecasting complex systems in different domains including energy, economics, biology, etc. One of this technique is the Fuzzy Inductive Reasoning (FIR), which has used for energy forecasting in residential buildings and smart grid [18; 19]. The consumption forecast plays an important role in predicting LNG storage evolution. In this regard, predicting the storage level allows to know if a consumer's demand for a time period can be satisfied while guaranteeing safety levels.