Application of Predictive Models for Natural Gas Needs - Current State and Future Trends Review

Nowadays, in terms of trading on the world scale, to foresee a natural gas consumption represents an essential activity. In the first part, the paper examines the current state of the Serbian natural gas sector and methodology applied for prediction and capacity planning. In addition, the study intends to give a comprehensive assessment of predictive algorithms for natural gas needs involved in the last decade with projections and suggestions for future applications. The primary task is to evaluate used predictive models with an emphasis on the accuracy of the predictions obtained. Additionally, the paper will analyse used parameters, consumption scale, prediction scope, forecast algorithms, and other related information. The main objective of this study is to review the new-fangled information related analyses data from peer-reviewed journals, international conferences, and books

Simulation of carbon peaking process of high energy consuming manufacturing industry in Shaanxi Province: A hybrid model based on LMDI and TentSSA-ENN

To achieve the goals of carbon peaking and carbon neutrality in Shaanxi, the high energy consuming manufacturing industry (HMI), as an important contributor, is a key link and important channel for energy conservation. In this paper, the logarithmic mean Divisia index (LMDI) method is applied to determine the driving factors of carbon emissions from the aspects of economy, energy and society, and the contribution of these factors was analyzed. Meanwhile, the improved sparrow search algorithm is used to optimize Elman neural network (ENN) to construct a new hybrid prediction model. Finally, three different development scenarios are designed using scenario analysis method to explore the potential of HMI in Shaanxi Province to achieve carbon peak in the future. The results show that: (1) The biggest promoting factor is industrial structure, and the biggest inhibiting factor is energy intensity among the drivers of carbon emissions, which are analyzed effectively in HMI using the LMDI method. (2) Compared with other neural network models, the proposed hybrid prediction model has higher accuracy and better stability in predicting industrial carbon emissions, it is more suitable for simulating the carbon peaking process of HMI. (3) Only in the coordinated development scenario, the HMI in Shaanxi is likely to achieve the carbon peak in 2030, and the carbon emission curve of the other two scenarios has not reached the peak. Then, according to the results of scenario analysis, specific and evaluable suggestions on carbon emission reduction for HMI in Shaanxi are put forward, such as optimizing energy and industrial structure and making full use of innovative resources of Shaanxi characteristic units

Factors affecting energy consumption and productivity in greenhouses

Aim of study: To investigate the impact factors affecting the greenhouse environment on energy consumption and productivity.Area of study: Alborz province of Iran during the period 2018–2020.Material and methods: In this study, 18 active units of greenhouse owners in Alborz province of Iran that had necessary standards were identified. Then, upper and lower amplitudes of the variables affecting productivity and energy consumption in greenhouses were calculated using a type-2 fuzzy neural network, Matlab 2017 software. Area, temperature, energy exchange, environmental evapotranspiration and relative humidity were studied as indicators.Main results: With each unit of temperature, energy consumption and productivity increased by 0.737% and 0.741%, respectively; with each unit of energy exchange, they increased by 0.813% and 0.696%, respectively; with each unit of evaporation and transpiration of the environment, they increased by 0.593% and 0.869%, respectively; and with each unit of humidity, they increased by 0.398% and 0.509%, respectively.Research highlights: The factors affecting the greenhouse environment such as area, temperature, evapotranspiration and relative humidity had a significant effect on productivity in studying greenhouses and therefore increasing their productivity. According to the results, the model’s ability in energy consumption was better than that for energy efficiency prediction. Also, greenhouse ranking was done by FAHP method

An empirical analysis on the credit scoring and the intermediary role of financing guarantee institutions of China's car loans

By the end of 2018, China's car ownership has reached 240 million, an increase of 10.51% over 2017, which leads to the increase of automobile financial services and hence the associated automobile credit risks. In order to transfer risks, financial institutions increasingly are choosing to issue auto loans through financing guarantee companies. Therefore, the industry pays more attention to the credit scoring, as it acts as the main risk control measure of auto financing guarantee companies. This leads to the study of the role the financing guarantee company plays and how effective the credit rating is as a risk control mechanism. The purpose is to investigate whether the auto financing guarantee company plays a mediating role by providing credit score. The empirical approach is as follows: a two-stage regression method is used to control or eliminate the influence of personal characteristics and other third-party credit ratings. Through which, we firstly test whether the credit score of an auto financing guarantee company contains additional information besides personal characteristics and third-party credit scores. Second, we test whether additional information of auto financing guarantee company can significantly explain the post-loan performance of whether default or non-default. The conclusions show that even after controlling the third-party credit score and personal characteristics, the credit scoring system of auto financing guarantee companies still has a significant explanation on the performance of post-loan default. In other words, it plays an intermediary role by providing credit evaluation services, which has a direct decision reference for the financial institutions that ultimately provide credit. Based on this, this study puts forward corresponding management enhancement and loan risk management suggestions.No final de 2018, a propriedade automóvel na China atingiu 240 milhões, um aumento de 10.51% sobre 2017, o que leva ao aumento dos serviços financeiros automóvel e, portanto, dos riscos de crédito automóvel associados. Para mitigar riscos, as instituições financeiras optam, cada vez mais, por conceder empréstimos automóvel através de empresas de garantia. Por conseguinte, a indústria presta mais atenção à pontuação do crédito, uma vez que esta atua como a principal medida de controlo do risco das empresas de garantia de financiamento-automóvel. Isto conduz ao estudo do papel desempenhado pela empresa de garantia de financiamento e da eficácia da sua notação de crédito como mecanismo de controlo dos riscos. Com base no sistema de notação de crédito da T’s e num total de 119.798 registos de empréstimos, este estudo examina o poder explicativo da notação de crédito das empresas de garantia de financiamento automóvel no incumprimento dos mutuários e as funções mediadoras destas empresas. Utiliza-se um método de regressão em dois estágios para controlar ou eliminar a influência de características pessoais e outros ratings, testando primeiro se a notação de crédito de uma empresa de garantia contém informações adicionais e testando, depois, se as informações adicionais da empresa de garantia podem explicar significativamente o desempenho do mutuário pós-empréstimo, As conclusões mostram que, mesmo após controlar a notação de crédito de terceiros e as características pessoais, o sistema de notação de crédito das empresas de garantia tem uma explicação significativa no desempenho do mutuário pós-empréstimo. Ou seja, ele desempenha um papel mediador, fornecendo serviços de avaliação de crédito que têm influência direta na decisão das instituições financeiras que, finalmente, fornecem crédito. Correspondentemente, esta investigação apresenta sugestões de melhoramento da gestão do risco de crédito

COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND ANFIS TO IMPROVE EEVE LABORATORY ENERGY USE PREDICTION PERFORMANCE

The energy use that is in excess of practicum students’ needs and the disturbed comfort that the practicum students experience when conducting practicums in the Electrical eengineering vocational education (EEVE) laboratory. The main objective in this study was to figure out how to predict and streamline the use of electrical energy in the EEVE laboratory. The model used to achieve this research’s goal was called the adaptive neurofuzzy inference system (ANFIS) model, which was coupled with principal component analysis (PCA) feature selection. The use of PCA in data grouping performance aims to improve the performance of the ANFIS model when predicting energy needs in accordance with the standards set by the campus while still taking students’ confidence in conducting practicum activities during campus operating hours into consideration. After some experiments and tests, very good results were obtained in the training: R=1 in training; minimum RMSE=0.011900; epoch of 100 per iteration; and R=0.37522. In conclusion, the ANFIS model coupled with PCA feature selection was excellent at predicting energy needs in the laboratory while the comfort of the students during practicums in the room remained within consideration

Forecasting methods in energy planning models

Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs

Improving the sustainability of coal SC in both developed and developing countries by incorporating extended exergy accounting and different carbon reduction policies

In the age of Industry 4.0 and global warming, it is inevitable for decision-makers to change the way they view the coal supply chain (SC). In nature, energy is the currency, and nature is the source of energy for humankind. Coal is one of the most important sources of energy which provides much-needed electricity, as well as steel and cement production. This manuscript-based PhD thesis examines the coal SC network as well as the four carbon reduction strategies and plans to develop a comprehensive model for sustainable design. Thus, the Extended Exergy Accounting (EEA) method is incorporated into a coal SC under economic order quantity (EOQ) and economic production quantity (EPQs) in an uncertain environment. Using a real case study in coal SC in Iran, four carbon reduction policies such as carbon tax (Chapter 5), carbon trade (Chapter 6), carbon cap (Chapter 7), and carbon offset (Chapter 8) are examined. Additionally, all carbon policies are compared for sustainable performance of coal SCs in some developed and developing countries (the USA, China, India, Germany, Canada, Australia, etc.) with the world's most significant coal consumption. The objective function of the four optimization models under each carbon policy is to minimize the total exergy (in Joules as opposed to Dollars/Euros) of the coal SC in each country. The models have been solved using three recent metaheuristic algorithms, including Ant lion optimizer (ALO), Lion optimization algorithm (LOA), and Whale optimization algorithm (WOA), as well as three popular ones, such as Genetic algorithm (GA), Ant colony optimization (ACO), and Simulated annealing (SA), are suggested to determine a near-optimal solution to an exergy fuzzy nonlinear integer-programming (EFNIP). Moreover, the proposed metaheuristic algorithms are validated by using an exact method (by GAMS software) in small-size test problems. Finally, through a sensitivity analysis, this dissertation compares the effects of applying different percentages of exergy parameters (capital, labor, and environmental remediation) to coal SC models in each country. Using this approach, we can determine the best carbon reduction policy and exergy percentage that leads to the most sustainable performance (the lowest total exergy per Joule). The findings of this study may enhance the related research of sustainability assessment of SC as well as assist coal enterprises in making logical and measurable decisions

Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review

To deliver electricity to customers safely and economically, power companies encounter numerous economic and technical challenges in their operations. Power flow analysis, planning, and control of power systems stand out among these issues. Over the last several years, one of the most developing study topics in this vital and demanding discipline has been electricity short-term load forecasting (STLF). Power system dispatching, emergency analysis, power flow analysis, planning, and maintenance all require it. This study emphasizes new research on long short-term memory (LSTM) algorithms related to particle swarm optimization (PSO) inside this area of short-term load forecasting. The paper presents an in-depth overview of hybrid networks that combine LSTM and PSO and have been effectively used for STLF. In the future, the integration of LSTM and PSO in the development of comprehensive prediction methods and techniques for multi-heterogeneous models is expected to offer significant opportunities. With an increased dataset, the utilization of advanced multi-models for comprehensive power load prediction is anticipated to achieve higher accuracy

Applications of machine learning to water resources management: A review of present status and future opportunities

Data availability: No data was used for the research described in the article.The corrected proof will be replaced by version of record in due course.Copyright © 2024 The Authors. Water is the most valuable natural resource on earth that plays a critical role in the socio-economic development of humans worldwide. Water is used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at a global scale, coupled with the predicted climate change-induced impacts, warrants the need for proactive and effective management of water resources. Over the recent decades, machine learning tools have been widely applied to various water resources management-related fields and have often shown promising results. Despite the publication of several review articles on machine learning applications in water-related fields, this review paper presents for the first time a comprehensive review of machine learning techniques applied to water resources management, focusing on the most recent achievements. The study examines the potential for advanced machine learning techniques to improve decision support systems in the various sectors within the realm of water resources management, which includes groundwater management, streamflow forecasting, water distribution systems, water quality and wastewater treatment, water demand and consumption, hydropower and marine energy, water drainage systems, and flood management and defence. This study provides an overview of the state-of-the-art machine learning approaches to the water industry and how they can be used to ensure water supply sustainability, quality, and flood and drought mitigation. This review covers the most recent related studies to provide the most recent snapshot of machine learning applications in the water industry. Overall, LSTM networks have been proven to exhibit reliable performance, often outperforming ANN models, traditional machine learning models, and established physics-based models. Hybrid ML techniques have exhibited great forecasting accuracy across all water-related fields, often showing superior computational power over traditional ANNs architectures. In addition to purely data-driven models, physical-based hybrid models have also been developed to improve prediction performance. These efforts further demonstrate that Machine learning can be a powerful practical tool for water resources management. It provides insights, predictions, and optimisation capabilities to help enhance sustainable water use and management and improve socio-economic development, healthy ecosystems and human existence.EPSRC project reference 2339403 to S. Sayed and A. Ahmed