Search results for: energy price forecasting

Authors: Joseph Levodo, Ndimbarafine Young Tobin, E. Messina, P. Edouma

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Energy efficiency can contribute significantly towards increasing clean energy access to modern energy services. Many developing countries have largely focused on expanding energy access by increasing supply. This is due to the fact the links between energy efficiency and clean energy access are often unnoticed. Energy efficiency measures offer the promise of reducing energy use and saving money on electricity bills, as well as reducing negative environmental externalities associated with the production of electricity. This paper seeks to address the economic and effectiveness of reducing energy consumption by integrating energy efficiency as a priority to meet energy access examines the barriers to energy efficient in sub-Saharan African countries. The findings from this study reveal that an appropriate policy can promote the development of more energy-efficient buildings, products and strengthen incentives for consumers, businesses, and industrial customers to pursue cost-effective energy-efficiency measures and to make investments that will provide future energy-efficiency improvements.

Keywords: barriers, Sub-Saharan Africa, cost effective, energy savings, clean energy

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Authors: Tai-Yue Wang, Yi-Ho Chen

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In this study, we investigate a two-echelon supply chain with two suppliers and three retailers among which one retailer dominates other retailers. A price competition demand function is used to model this dominant retailer, which is leading market. The promotion strategies and negotiation schemes are integrated to form decision-making models under different scenarios. These models are then formulated into different mathematical programming models. The decision variables such as promotional costs, retailer prices, wholesale price, and order quantity are included in these models. At last, the distributions of promotion costs under different cost allocation strategies are discussed. Finally, an empirical example used to validate our models. The results from this empirical example show that the profit model will create the largest profit for the supply chain but with different profit-sharing results. At the same time, the more risk a member can take, the more profits are distributed to that member in the utility model.

Keywords: supply chain, price promotion, mathematical models, dominant retailer

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Authors: Stephanie Fabris Russo, Daiane Costa Guimarães, Jonas Pedro Fabris, Maria Emilia Camargo, Suzana Leitão Russo, José Augusto Andrade Filho

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Among renewable energy sources, solar energy is the one that has stood out. Solar radiation can be used as a thermal energy source and can also be converted into electricity by means of effects on certain materials, such as thermoelectric and photovoltaic panels. These panels are often used to generate energy in homes, buildings, arenas, etc., and have low pollution emissions. Thus, a technological prospecting was performed to find patents related to the use of photovoltaic plates in urban residences. The patent search was based on ESPACENET, associating the keywords photovoltaic and home, where we found 136 patent documents in the period of 1994-2015 in the fields title and abstract. Note that the years 2009, 2010, 2011, 2012, 2013 and 2014 had the highest number of applicants, with respectively, 11, 13, 23, 29, 15 and 21. Regarding the country that deposited about this technology, it is clear that China leads with 67 patent deposits, followed by Japan with 38 patents applications. It is important to note that most depositors, 50% are companies, 44% are individual inventors and only 6% are universities. On the International Patent classification (IPC) codes, we noted that the most present classification in results was H02J3/38, which represents provisions in parallel to feed a single network by two or more generators, converters or transformers. Among all categories, there is the H session, which means Electricity, with 70% of the patents.

Keywords: photovoltaic, urban residences, technology forecasting, prospecting

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Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

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Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

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Authors: Naci Büyükkaracığan, Murat Ahmet Ökmen

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Emerging demand for energy in developing countries rapid population growth and industrialization are causing a rapid increase, such as Turkey. Energy is an important and indispensable factor in the industry. At the same time, energy is one of the main indicators that reflect a country's economic and social development potential. There is a linear relationship between the energy consumption and social development, and in parallel this situation, it is seen that energy consumption increase with economic growth and prosperity. In recent year’s, energy sources consumption is increasingly continuing, because of population growth and economy in Turkey. 80% of the energy used in Turkey is supplied from abroad. At the same time, while almost all of the energy obtained from our country is met by hydropower. Alternatively, studies of determining and using potential renewable energy sources such as solar energy have been realized for recent years. In this study, first of all, the situation of energy sources was examined in Turkey. Information of reserve/capacity, production and consumption values of energy sources were emphasized. For this purpose, energy production and consumption, CO2 emission and electricity energy consumption of countries were investigated. Energy consumption and electricity energy consumption per capita were comparatively analyzed.

Keywords: energy potential, alternative energy sources, solar energy, Turkey

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Authors: Pierluigi Gallo, Francesco Randazzo, Ignazio Gallo

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In recent years, many new and interesting models of successful online business have been developed. Many of these are based on the competition between users, such as online auctions, where the product price is not fixed and tends to rise. Other models, including group-buying, are based on cooperation between users, characterized by a dynamic price of the product that tends to go down. There is not yet a business model in which both sellers and buyers are grouped in order to negotiate on a specific product or service. The present study investigates a new extension of the group-buying model, called fair, which allows aggregation of demand and supply for price optimization, in a cooperative manner. Additionally, our system also aggregates products and destinations for shipping optimization. We introduced the following new relevant input parameters in order to implement a double-side aggregation: (a) price-quantity curves provided by the seller; (b) waiting time, that is, the longer buyers wait, the greater discount they get; (c) payment time, which determines if the buyer pays before, during or after receiving the product; (d) the distance between the place where products are available and the place of shipment, provided in advance by the buyer or dynamically suggested by the system. To analyze the proposed model we implemented a system prototype and a simulator that allows studying effects of changing some input parameters. We analyzed the dynamic price model in fairs having one single seller and a combination of selected sellers. The results are very encouraging and motivate further investigation on this topic.

Keywords: auction, aggregation, fair, group buying, social buying

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Authors: Marinko Skare, Malgorzata Porada Rochon

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This paper study the link between energy justice and economic growth. The link between energy justice and growth has not been extensively studied. Here we study the impact and importance of energy justice, as a part of the energy transition process, on economic growth. Our study shows energy justice growth is an important determinant of economic growth and development that should be addressed at the industry and economic levels. We use panel data modeling and causality testing to research the empirical link between energy justice and economic growth. Industry and economy-level policies designed to support energy justice initiatives are beneficial to economic growth. Energy justice is a necessary condition for green growth and sustainability targets.

Keywords: energy justice, economic growth, panel data, energy transition

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Authors: Himayatullah Khan

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This study estimates supply response function of sugarcane in Pakistan from 1980-81 to 2012-13. The study uses co-integration approach and error correction mechanism. Sugarcane production, area and price series were tested for unit root using Augmented Dickey Fuller (ADF). The study found that these series were stationary at their first differenced level. Using the Augmented Engle-Granger test and Cointegrating Regression Durbin-Watson (CRDW) test, the study found that “production and price” and “area and price” were co-integrated suggesting that the two sets of time series had long-run or equilibrium relationship. The results of the error correction models for the two sets of series showed that there was disequilibrium in the short run there may be disequilibrium. The Engle-Granger residual may be thought of as the equilibrium error which can be used to tie the short-run behavior of the dependent variable to its long-run value. The Granger-Causality test results showed that log of price granger caused both the long of production and log of area whereas, the log of production and log of area Granger caused each other.

Keywords: co-integration, error correction mechanism, Granger-causality, sugarcane, supply response

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Authors: Cristian Pauna

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Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

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Authors: Edson Vengesai

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Derivative products have become essential instruments in portfolio diversification, price discovery, and, most importantly, risk hedging. Derivatives are complex instruments; their valuation, volatility implications, and real impact on the underlying assets' behaviour are not well understood. Little is documented empirically, with conflicting conclusions on how these instruments affect firm risk exposures. Given the growing interest in using derivatives in risk management and portfolio engineering, this study examines the practical impact of derivative usage on the underlying stock price exposure and systematic risk. The paper uses data from South African listed firms. The study employs GARCH models to understand the effect of derivative uses on conditional stock volatility. The GMM models are used to estimate the effect of derivatives use on stocks' systematic risk as measured by Beta and on the total risk of stocks as measured by the standard deviation of returns. The results provide evidence on whether derivatives use is instrumental in reducing stock returns' systematic and total risk. The results are subjected to numerous controls for robustness, including financial leverage, firm size, growth opportunities, and macroeconomic effects.

Keywords: derivatives use, hedging, volatility, stock price exposure

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Authors: Zhaojun Wang, Zongdi Sun, Yongjie Chen, Manman Chen, Linghui Wang

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This paper uses the analytic hierarchy process to study the execution effect of the minimum purchase price of grain in different regions and various grain crops. Firstly, for different regions, five indicators including grain yield, grain sown area, gross agricultural production, grain consumption price index, and disposable income of rural residents were selected to construct an evaluation index system. We collect data of six provinces including Hebei Province, Heilongjiang Province and Shandong Province from 2006 to 2017. Then, the judgment matrix is constructed, and the hierarchical single ordering and consistency test are carried out to determine the scoring standard for the minimum purchase price of grain. The ranking of the execution effect from high to low is: Heilongjiang Province, Shandong Province, Hebei Province, Guizhou Province, Shaanxi Province, and Guangdong Province. Secondly, taking Shandong Province as an example, we collect the relevant data of sown area and yield of cereals, beans, potatoes and other crops from 2006 to 2017. The weight of area and yield index is determined by expert scoring method. And the average sown area and yield of cereals, beans and potatoes in 2006-2017 were calculated, respectively. On this basis, according to the sum of products of weights and mean values, the execution effects of different grain crops are determined. It turns out that among the cereals, the minimum purchase price had the best execution effect on paddy, followed by wheat and finally maize. Moreover, among major categories of crops, cereals perform best, followed by beans and finally potatoes. Lastly, countermeasures are proposed for different regions, various categories of crops, and different crops of the same category.

Keywords: analytic hierarchy process, grain yield, grain sown area, minimum grain purchase price

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Authors: Halefom Kidane

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This study aims to assess the wind resource potential and characterize the urban area wind patterns in Hawassa City, Ethiopia. The estimation and characterization of wind resources are crucial for sustainable urban planning, renewable energy development, and climate change mitigation strategies. A secondary data collection method was used to carry out the study. The collected data at 2 meters was analyzed statistically and extrapolated to the standard heights of 10-meter and 30-meter heights using the power law equation. The standard deviation method was used to calculate the value of scale and shape factors. From the analysis presented, the maximum and minimum mean daily wind speed at 2 meters in 2016 was 1.33 m/s and 0.05 m/s in 2017, 1.67 m/s and 0.14 m/s in 2018, 1.61m and 0.07 m/s, respectively. The maximum monthly average wind speed of Hawassa City in 2016 at 2 meters was noticed in the month of December, which is around 0.78 m/s, while in 2017, the maximum wind speed was recorded in the month of January with a wind speed magnitude of 0.80 m/s and in 2018 June was maximum speed which is 0.76 m/s. On the other hand, October was the month with the minimum mean wind speed in all years, with a value of 0.47 m/s in 2016,0.47 in 2017 and 0.34 in 2018. The annual mean wind speed was 0.61 m/s in 2016,0.64, m/s in 2017 and 0.57 m/s in 2018 at a height of 2 meters. From extrapolation, the annual mean wind speeds for the years 2016,2017 and 2018 at 10 heights were 1.17 m/s,1.22 m/s, and 1.11 m/s, and at the height of 30 meters, were 3.34m/s,3.78 m/s, and 3.01 m/s respectively/Thus, the site consists mainly primarily classes-I of wind speed even at the extrapolated heights.

Keywords: artificial neural networks, forecasting, min-max normalization, wind speed

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Authors: B. E. Weber, N. Vrielink, M. G. Rietbergen

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This paper explores the long-term effects of the Energy Box trajectory on households in the private rental sector, specifically households experiencing energy poverty. The concept of energy poverty has been getting increasing attention among policymakers over the past few years. In the Netherlands, as far as we know, there are no national policies on alleviating energy poverty, which negatively impacts energy-poor households. The Energy Box can help households experiencing energy poverty by stimulating them to improve the energy efficiency of their home by changing their energy-saving behavior. Important long-term effects are that respondents indicate that they live in a more environmentally friendly way and that they save money on their energy bills. Households feel engaged with the concept of energy-saving and can see the benefits of changing their energy-saving behavior. Respondents perceived the Energy Box as a means to live more environmentally friendly, instead of it solely being a means to save money on energy bills. The findings show that most respondents signed up for the Energy Box are interested in energy-saving as a lifestyle choice instead of a financial choice, which would likely be the case for households experiencing energy poverty.

Keywords: energy-saving behavior, energy poverty, poverty, private rental sector

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Authors: Liping Yang, Curran Crawford, Jr. Ren, Zhengyi Ren

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Considering the cost evaluation and the stress analysis, a fuzzy satisfactory optimization (FSO) method has been developed for a hybrid composite flywheel. To evaluate the cost, the cost coefficients of the flywheel components are obtained through calculating the weighted sum of the scores of the material manufacturability, the structure character, and the material price. To express the satisfactory degree of the energy, the cost, and the mass, the satisfactory functions are proposed by using the decline function and introducing a satisfactory coefficient. To imply the different significance of the objectives, the object weight coefficients are defined. Based on the stress analysis of composite material, the circumferential and radial stresses are considered into the optimization formulation. The simulations of the FSO method with different weight coefficients and storage energy density optimization (SEDO) method of a flywheel are contrasted. The analysis results show that the FSO method can satisfy different requirements of the designer and the FSO method with suitable weight coefficients can replace the SEDO method.

Keywords: flywheel energy storage, fuzzy, optimization, stress analysis

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Authors: Hassan Adedoyin Rasaq

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The marketing efforts of the present day business is not just geared towards meeting the consumer’s needs at a price, but ensuring good customer satisfaction, and strategizing on how to retain such customers. Customer satisfaction and retention is achievable through the co-ordination of the marketing mixes; Product, Price, Promotion and Place; Relationship Marketing; After-Sales Service; Rebates/Discounts/Price reduction policy and Total Quality Management (TQM). A first-hand customer, If well satisfied, will become a company’s repeat customer, proceeds to become a client and goes further to become an advocate of the company by applauding the company’s products/services and encouraging others to buy from it. It is the objective of this paper, therefore, to guide business organizations on how to enhance customer satisfaction, and retain existing customers as a means of long-term survival in marketing. The responses of 72 randomly selected Marketing personnel spread across three (3) food and beverage companies in Nigeria were analyzed. One hypothesis was tested using a one-way analysis of variance (ANOVA) statistical tool, and it was discovered that Relationship marketing contributed to organizational profitability and growth.

Keywords: customer satisfaction, retention strategies, marketing, marketing mixes

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

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Authors: C. Fúnez Guerra, B. Nieto Calderón, M. Jaén Caparrós, L. Reyes-Bozo, A. Godoy-Faúndez, E. Vyhmeister

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The global energy system is experiencing a change of scenery. Unstable energy markets, an increasing focus on climate change and its sustainable development is forcing businesses to pursue new solutions in order to ensure future economic growth. This has led to the interest in using hydrogen as an energy carrier in transportation and industrial applications. As an energy carrier, hydrogen is accessible and holds a high gravimetric energy density. Abundant in hydrocarbons, hydrogen can play an important role in the shift towards low-emission fossil value chains. By combining hydrogen production by natural gas reforming with carbon capture and storage, the overall CO2 emissions are significantly reduced. In addition, the flexibility of hydrogen as an energy storage makes it applicable as a stabilizer in the renewable energy mix. The recent development in hydrogen fuel cells is also raising the expectations for a hydrogen powered transportation sector. Hydrogen value chains exist to a large extent in the industry today. The global hydrogen consumption was approximately 50 million tonnes (7.2 EJ) in 2013, where refineries, ammonia, methanol production and metal processing were main consumers. Natural gas reforming produced 48% of this hydrogen, but without carbon capture and storage (CCS). The total emissions from the production reached 500 million tonnes of CO2, hence alternative production methods with lower emissions will be necessary in future value chains. Hydrogen from electrolysis is used for a wide range of industrial chemical reactions for many years. Possibly, the earliest use was for the production of ammonia-based fertilisers by Norsk Hydro, with a test reactor set up in Notodden, Norway, in 1927. This application also claims one of the world’s largest electrolyser installations, at Sable Chemicals in Zimbabwe. Its array of 28 electrolysers consumes 80 MW per hour, producing around 21,000 Nm3/h of hydrogen. These electrolysers can compete if cheap sources of electricity are available and natural gas for steam reforming is relatively expensive. Because electrolysis of water produces oxygen as a by-product, a system of Autothermal Reforming (ATR) utilizing this oxygen has been analyzed. Replacing the air separation unit with electrolysers produces the required amount of oxygen to the ATR as well as additional hydrogen. The aim of this paper is to evaluate the technical and economic potential of large-scale production of hydrogen for oil refining industry. Sensitivity analysis of parameters such as investment costs, plant operating hours, electricity price and sale price of hydrogen and oxygen are performed.

Keywords: autothermal reforming, electrolyser, hydrogen, natural gas, steam methane reforming

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Authors: Janne Engblom, Elias Oikarinen

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The understanding of housing price dynamics is of importance to a great number of agents: to portfolio investors, banks, real estate brokers and construction companies as well as to policy makers and households. A panel dataset is one that follows a given sample of individuals over time, and thus provides multiple observations on each individual in the sample. Panel data models include a variety of fixed and random effects models which form a wide range of linear models. A special case of panel data models is dynamic in nature. A complication regarding a dynamic panel data model that includes the lagged dependent variable is endogeneity bias of estimates. Several approaches have been developed to account for this problem. In this paper, the panel models were estimated using the Common Correlated Effects estimator (CCE) of dynamic panel data which also accounts for cross-sectional dependence which is caused by common structures of the economy. In presence of cross-sectional dependence standard OLS gives biased estimates. In this study, U.S housing price dynamics were examined empirically using the dynamic CCE estimator with first-difference of housing price as the dependent and first-differences of per capita income, interest rate, housing stock and lagged price together with deviation of housing prices from their long-run equilibrium level as independents. These deviations were also estimated from the data. The aim of the analysis was to provide estimates with comparisons of estimates between 1980-1999 and 2000-2012. Based on data of 50 U.S cities over 1980-2012 differences of short-run housing price dynamics estimates were mostly significant when two time periods were compared. Significance tests of differences were provided by the model containing interaction terms of independents and time dummy variable. Residual analysis showed very low cross-sectional correlation of the model residuals compared with the standard OLS approach. This means a good fit of CCE estimator model. Estimates of the dynamic panel data model were in line with the theory of housing price dynamics. Results also suggest that dynamics of a housing market is evolving over time.

Keywords: dynamic model, panel data, cross-sectional dependence, interaction model

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Authors: Yamini Raju, Parminder S. Kang, Adam Moroz, Ross Clement, Alistair Duffy, Ashley Hopwell

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Accurate prediction of fresh produce demand is one the challenges faced by Small Medium Enterprise (SME) wholesalers. Current research in this area focused on limited number of factors specific to a single product or a business type. This paper gives an overview of the current literature on the variability factors used to predict demand and the existing forecasting techniques of short shelf life products. It then extends it by adding new factors and investigating if there is a time lag and possibility of noise in the orders. It also identifies the most important factors using correlation and Principal Component Analysis (PCA).

Keywords: demand forecasting, deteriorating products, food wholesalers, principal component analysis, variability factors

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: air quality prediction, deep learning algorithms, time series forecasting, look-back window

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Authors: Elvira S. Castillo, Surupa Shaw

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This paper explores advanced applications of fluid mechanics in the context of sustainable energy. By examining the integration of fluid dynamics with renewable energy technologies, the research uncovers previously underutilized strategies for improving efficiency. Through theoretical analyses, the study demonstrates how fluid mechanics can be harnessed to optimize renewable energy systems. The findings contribute to expanding knowledge in sustainable energy by offering practical insights and methodologies for future research and technological advancements to address global energy challenges.

Keywords: fluid mechanics, sustainable energy, energy efficiency, green energy

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Authors: Aigerim Uyzbayeva, Valeriya Tyo, Nurlan Ibrayev

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Kazakhstan is currently one of the dynamically developing states in its region. The stable growth in all sectors of the economy leads to a corresponding increase in energy consumption. Thus, country consumes a significant amount of energy due to the high level of industralisation and the presence of energy-intensive manufacturing such as mining and metallurgy which in turn leads to low energy efficiency. With allowance for this the Government has set several priorities to adopt a transition of Republic of Kazakhstan to a “green economy”. This article provides an overview of Kazakhstan’s energy efficiency situation in for the period of 1991-2014. First, the dynamics of production and consumption of conventional energy resources are given. Second, the potential of renewable energy sources is summarised, followed by the description of GHG emissions trends in the country. Third, Kazakhstan’ national initiatives, policies and locally implemented projects in the field of energy efficiency are described.

Keywords: energy efficiency in Kazakhstan, greenhouse gases, renewable energy, sustainable development

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Authors: Fahim Ullah, Muhammad Usman

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Energy consumption and environmental sustainability are hard challenges of 21st century. Energy richness increases environmental pollution while energy poverty hinders economic growth. Considering these two aspects, present study calculates energy deficiency and examines the role of renewable energy to overcome rising energy deficiency and carbon emission for selected countries from 1990 to 2021. For empirical analysis, this study uses methods of moments panel quantile regression analysis and to check the robustness, study used panel quantile robust analysis. Graphical analysis indicated rising global energy deficiency since last three decades where energy consumption is higher than energy production. Empirical results showed that renewable energy is a significant factor for reducing energy deficiency. Secondly, the energy deficiency increases carbon emission level and again renewable energy decreases emissions level. This study recommends that global energy deficiency and rising carbon emissions can be controlled through structural change in the form of energy transition to replace non-renewable resources with renewable resources.

Keywords: energy deficiency, renewable energy, carbon emission, energy trade, PQL analysis

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Authors: Navid Mohammadzadeh, Huy Truong-Ba, Michael Cholette

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The solar tower (ST) plant is a promising technology to exploit large-scale solar irradiation. With thermal energy storage, ST plant has the potential to shift generation to high electricity price periods. However, the size of storage limits the dispatchability of the plant, particularly when it should compete with uncertainty in forecasts of solar irradiation and electricity prices. The purpose of this study is to explore the size of storage when Rolling Horizon Control (RHC) is employed for dispatch scheduling. To this end, RHC is benchmarked against perfect knowledge (PK) forecast and two day-ahead dispatching policies. With optimisation of dispatch planning using PK policy, the optimal achievable profit for a specific size of the storage is determined. A sensitivity analysis using Monte-Carlo simulation is conducted, and the size of storage for RHC and day-ahead policies is determined with the objective of reaching the profit obtained from the PK policy. A case study is conducted for a hypothetical ST plant with thermal storage located in South Australia and intends to dispatch under two market scenarios: 1) fixed price and 2) wholesale spot price. The impact of each individual source of uncertainty on storage size is examined for January and August. The exploration of results shows that dispatching with RH controller reaches optimal achievable profit with ~15% smaller storage compared to that in day-ahead policies. The results of this study may be applied to the CSP plant design procedure.

Keywords: solar tower plant, spot market, thermal storage system, optimized dispatch planning, sensitivity analysis, Monte Carlo simulation

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Authors: Lubna Naz, Munir Ahmad, G. M. Arif

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This study analyzes static and dynamic welfare impacts of food price changes for various landholding groups in Pakistan. The study uses three classifications of land ownership, landless, small landowners and large landowners, for analysis. The study uses Panel Survey, Pakistan Rural Household Survey (PRHS) of Pakistan Institute of Development Economics Islamabad, of rural households from two largest provinces (Sindh and Punjab) of Pakistan. The study uses all three waves (2001, 2004 and 2010) of PRHS. This research work makes three important contributions in literature. First, this study uses Quadratic Almost Ideal Demand System (QUAIDS) to estimate demand functions for eight food groups-cereals, meat, milk and milk products, vegetables, cooking oil, pulses and other food. The study estimates food demand functions with Nonlinear Seemingly Unrelated (NLSUR), and employs Lagrange Multiplier and test on the coefficient of squared expenditure term to determine inclusion of squared expenditure term. Test results support the inclusion of squared expenditure term in the food demand model for each of landholding groups (landless, small landowners and large landowners). This study tests for endogeneity and uses control function for its correction. The problem of observed zero expenditure is dealt with a two-step procedure. Second, it creates low price and high price periods, based on literature review. It uses elasticity coefficients from QUAIDS to analyze static and dynamic welfare effects (first and second order Tylor approximation of expenditure function is used) of food price changes across periods. The study estimates compensation variation (CV), money metric loss from food price changes, for landless, small and large landowners. Third, this study compares the findings on welfare implications of food price changes based on QUAIDS with the earlier research in Pakistan, which used other specification of the demand system. The findings indicate that dynamic welfare impacts of food price changes are lower as compared to static welfare impacts for all landholding groups. The static and dynamic welfare impacts of food price changes are highest for landless. The study suggests that government should extend social security nets to landless poor and categorically to vulnerable landless (without livestock) to redress the short-term impact of food price increase. In addition, the government should stabilize food prices and particularly cereal prices in the long- run.

Keywords: QUAIDS, Lagrange multiplier, NLSUR, and Tylor approximation

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Authors: Gurmail Singh

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Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

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Authors: Mark Andrew

Abstract:

Geopolitical tensions are at a thirty-year high, and the pace of technological innovation is driving asymmetry in force capabilities between nation states and between non-state actors. Technology futures are a vital component of defence capability growth, and investments in technology futures need to be informed by accurate and reliable forecasts of the options for ‘systems of systems’ innovation, development, and deployment. This paper describes a method for forecasting technology futures developed through an analysis of four key systems’ development stages, namely: technology domain categorisation, scanning results examining novel systems’ signals and signs, potential system-of systems’ implications in warfare theatres, and political ramifications in terms of funding and development priorities. The method has been applied to several technology domains, including physical systems (e.g., nano weapons, loitering munitions, inflight charging, and hypersonic missiles), biological systems (e.g., molecular virus weaponry, genetic engineering, brain-computer interfaces, and trans-human augmentation), and information systems (e.g., sensor technologies supporting situation awareness, cyber-driven social attacks, and goal-specification challenges to proliferation and alliance testing). Although the current application of the method has been team-centred using paper-based rapid prototyping and iteration, the application of autonomous language models (such as GPT-3) is anticipated as a next-stage operating platform. The importance of forecasting accuracy and reliability is considered a vital element in guiding technology development to afford stronger contingencies as ideological changes are forecast to expand threats to ecology and earth systems, possibly eclipsing the traditional vulnerabilities of nation states. The early results from the method will be subjected to ground truthing using longitudinal investigation.

Keywords: forecasting, technology futures, uncertainty, complexity

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Authors: Si Chen, Le Zhang, Xian He

Abstract:

The research explored the influences of public open space system on housing price through hedonic models, in order to support better open space plans and economic policies. We have three initial hypotheses: 1) public open space system has an overall positive influence on surrounding housing prices. 2) Different public open space types have different levels of influence on motivating surrounding housing prices. 3) Walking and driving accessibilities from property to public open spaces have different statistical relation with housing prices. Cook County, Illinois, was chosen to be a study area since data availability, sufficient open space types, and long-term open space preservation strategies. We considered the housing attributes, driving and walking accessibility scores from houses to nearby public open spaces, and driving accessibility scores to hospitals as influential features and used real housing sales price in 2010 as a dependent variable in the built hedonic model. Through ordinary least squares (OLS) regression analysis, General Moran’s I analysis and geographically weighted regression analysis, we observed the statistical relations between public open spaces and housing sale prices in the three built hedonic models and confirmed all three hypotheses.

Keywords: hedonic model, public open space, housing sale price, regression analysis, accessibility score

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Authors: Ulrike Dowie, Ralph Grothmann

Abstract:

Rational planning decisions are based upon forecasts. Precise forecasting has, therefore, a central role in business. The prediction of customer demand is a prime example. This paper introduces recurrent neural networks to model customer demand and combines the forecast with uncertainty measures to derive decision support of the demand planning department. It identifies and describes the keys to the successful implementation of an AI-based solution: bringing together data with business knowledge, AI methods, and user experience, and applying agile software development practices.

Keywords: agile software development, AI project success factors, deep learning, demand forecasting, forecast uncertainty, neural networks, supply chain management

Procedia PDF Downloads 189

Authors: Yasaman Ashrafi, Stephen Kajewski, Annastiina Silvennoinen, Madhav Nepal

Abstract:

Mega projects either in construction, urban development or energy sectors are one of the key drivers that build the foundation of wealth and modern civilizations in regions and nations. Such projects require economic justification and substantial capital investment, often derived from individual and corporate investors as well as governments. Cost overruns and time delays in these mega projects demands a new approach to more accurately predict project costs and establish realistic financial plans. The significance of this paper is that the cost efficiency of megaprojects will improve and decrease cost overruns. This research will assist Project Managers (PMs) to make timely and appropriate decisions about both cost and outcomes of ongoing projects. This research, therefore, examines the oil and gas industry where most mega projects apply the classic methods of Cost Performance Index (CPI) and Schedule Performance Index (SPI) and rely on project data to forecast cost and time. Because these projects are always overrun in cost and time even at the early phase of the project, the probabilistic methods of Monte Carlo Simulation (MCS) and Bayesian Adaptive Forecasting method were used to predict project cost at completion of projects. The current theoretical and mathematical models which forecast the total expected cost and project completion date, during the execution phase of an ongoing project will be evaluated. Earned Value Management (EVM) method is unable to predict cost at completion of a project accurately due to the lack of enough detailed project information especially in the early phase of the project. During the project execution phase, the Bayesian adaptive forecasting method incorporates predictions into the actual performance data from earned value management and revises pre-project cost estimates, making full use of the available information. The outcome of this research is to improve the accuracy of both cost prediction and final duration. This research will provide a warning method to identify when current project performance deviates from planned performance and crates an unacceptable gap between preliminary planning and actual performance. This warning method will support project managers to take corrective actions on time.

Keywords: cost forecasting, earned value management, project control, project management, risk analysis, simulation

Procedia PDF Downloads 403