Search results for: electricity demand forecasting

Authors: L. L. Ivy-Yap, H. A. Bekhet

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As the Malaysian residential electricity consumption continued to increase rapidly, effective energy policies, which address factors affecting residential electricity consumption, is urgently needed. This study attempts to investigate the relationship between residential electricity consumption (EC), real disposable income (Y), price of electricity (Pe) and population (Po) in Malaysia for 1978-2011 periods. Unlike previous studies on Malaysia, the current study focuses on the residential sector, a sector that is important for the contemplation of energy policy. The Phillips-Perron (P-P) unit root test is employed to infer the stationary of each variable while the bound test is executed to determine the existence of co-integration relationship among the variables, modeled in an Autoregressive Distributed Lag (ARDL) framework. The CUSUM and CUSUM of squares tests are applied to ensure the stability of the model. The results suggest the existence of long-run equilibrium relationship and bidirectional Granger causality between EC and the macroeconomic variables. The empirical findings will help policy makers of Malaysia in developing new monitoring standards of energy consumption. As it is the major contributing factor in economic growth and CO2 emission, there is a need for more proper planning in Malaysia to attain future targets in order to cut emissions.

Keywords: co-integration, elasticity, granger causality, Malaysia, residential electricity consumption

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Authors: Geng Cui, Naoto Imura, Katsuhiro Nishinari, Takahiro Ezaki

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The bullwhip effect, which refers to the amplification of demand variance as one moves up the supply chain, has been observed in various industries and extensively studied through analytic approaches. Existing methods to mitigate the bullwhip effect, such as decentralized demand information, vendor-managed inventory, and the Collaborative Planning, Forecasting, and Replenishment System, rely on the willingness and ability of supply chain participants to share their information. However, in practice, information sharing is often difficult to realize due to privacy concerns. The purpose of this study is to explore new ways to mitigate the bullwhip effect without the need for information sharing. This paper proposes a 'bullwhip absorption strategy' (BAS) to alleviate the bullwhip effect by absorbing it downstream in the supply chain. To achieve this, a two-stage supply chain system was employed, consisting of a single retailer and a single manufacturer. In each time period, the retailer receives an order generated according to an autoregressive process. Upon receiving the order, the retailer depletes the ordered amount, forecasts future demand based on past records, and places an order with the manufacturer using the order-up-to replenishment policy. The manufacturer follows a similar process. In essence, the mechanism of the model is similar to that of the beer game. The BAS is implemented at the retailer's level to counteract the bullwhip effect. This strategy requires the retailer to reduce the uncertainty in its orders, thereby absorbing the bullwhip effect downstream in the supply chain. The advantage of the BAS is that upstream participants can benefit from a reduced bullwhip effect. Although the retailer may incur additional costs, if the gain in the upstream segment can compensate for the retailer's loss, the entire supply chain will be better off. Two indicators, order variance and inventory variance, were used to quantify the bullwhip effect in relation to the strength of absorption. It was found that implementing the BAS at the retailer's level results in a reduction in both the retailer's and the manufacturer's order variances. However, when examining the impact on inventory variances, a trade-off relationship was observed. The manufacturer's inventory variance monotonically decreases with an increase in absorption strength, while the retailer's inventory variance does not always decrease as the absorption strength grows. This is especially true when the autoregression coefficient has a high value, causing the retailer's inventory variance to become a monotonically increasing function of the absorption strength. Finally, numerical simulations were conducted for verification, and the results were consistent with our theoretical analysis.

Keywords: bullwhip effect, supply chain management, inventory management, demand forecasting, order-to-up policy

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Authors: Jaroslav Frantík, Jan Najser

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This article deals with increasing the energy efficiency of a plant in terms of optimizing the process. The European Union is striving to achieve the climate-energy package in the area increasing of energy efficiency. The goal of energy efficiency is to reduce primary energy consumption by 20% within the EU until 2020. The objective of saving energy consumption in the Czech Republic was set at 47.84 PJ (13.29 TWh). For reducing electricity consumption, it is possible to choose: a) mandatory increasing of energy efficiency, b) alternative scheme, c) combination of both actions. The Czech Republic has chosen for reducing electricity consumption using-alternative scheme. The presentation is focused on the proposal of a technological unit dealing with the gasification process of processing of biomass with an increase of power in the output. The synthesis gas after gasification of biomass is used as fuel in a cogeneration process of reciprocating internal combustion engine with the classic production of heat and electricity. Subsequently, there is an explanation of the ORC system dealing with the conversion of waste heat to electricity with the using closed cycle of the steam process with organic medium. The arising electricity is distributed to the power grid as a further energy source, or it is used for needs of the partial coverage of the technological unit. Furthermore, there is a presented schematic description of the technology with the identification of energy flows starting from the biomass treatment by drying, through its conversion to gaseous fuel, producing of electricity and utilize of thermal energy with minimizing losses. It has been found that using of ORC system increased the efficiency of the produced electricity by 7.5%.

Keywords: biomass, efficiency, gasification, ORC system

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Authors: A. Kumar, V. Devadas

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Energy is the basic element for any country’s economic development. India is one of the most populated countries, and is dependent on fossil fuel and nuclear-based energy generation. The energy sector faces huge challenges and is dependent on the import of energy from neighboring countries to fulfill the gap in demand and supply. India has huge setbacks for efficient energy generation, distribution, and consumption, therefore they consume more quantity of energy to produce the same amount of Gross Domestic Product (GDP) compared to the developed countries. Technology and technique use, availability, and affordability in the various sectors are varying according to their economic status. In this paper, an attempt is made to quantify the domestic electrical energy consumption in Jodhpur, India. Survey research methods have been employed and stratified sampling technique-based households were chosen for conducting the investigation. Pre-tested survey schedules are used to investigate the grassroots level study. The collected data are analyzed by employing statistical techniques. Thereafter, a multiple regression model is developed to understand the functions of total electricity consumption in the domestic sector corresponding to other independent variables including electrical appliances, age of the building, household size, education, etc. The study resulted in identifying the governing variable in energy consumption at the household level and their relationship with the efficiency of household-based electrical and energy appliances. The analysis is concluded with the recommendation for optimizing the gap in peak electrical demand and supply in the domestic sector.

Keywords: appliance, consumption, electricity, households

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Authors: Andreas Gohs, Reinhold Kosfeld

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This study should be of interest for practitioners who want to predict precisely the sales numbers of vehicles equipped with an innovative propulsion technology as well as for researchers interested in applied (regional) time series analysis. The study is based on the numbers of new registrations of pure electric and hybrid cars. Methods of time series analysis like ARIMA are compared with the Bass Diffusion-model concerning their forecasting performances for new registrations in Germany at the national and federal state levels. Especially it is investigated if the additional information content from regional data increases the forecasting accuracy for the national level by adding predictions for the federal states. Results of parameters of the Bass Diffusion Model estimated for Germany and its sixteen federal states are reported. While the focus of this research is on the German market, estimation results are also provided for selected European and other countries. Concerning Bass-parameters and forecasting performances, we get very different results for Germany's federal states and the member states of the European Union. This corresponds to differences across the EU-member states in the adoption process of this innovative technology. Concerning the German market, the adoption is rather proceeded in southern Germany and stays behind in Eastern Germany except for Berlin.

Keywords: bass diffusion model, electric vehicles, forecasting performance, market diffusion

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Authors: Khaoula Bensaida, Osama Eljamal

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The electrical energy generation through Microbial Fuel Cells (MFCs) using microorganisms is a renewable and sustainable approach. It creates truly an efficient technology for power production and wastewater treatment. MFC is an electrochemical device which turns wastewater into electricity. The most important part of MFC is microbes. Nano zero-valent Iron NZVI technique was successfully applied in degrading the chemical pollutants and cleaning wastewater. However, the use of NZVI for enhancing the current production is still not confirmed yet. This study aims to confirm the effect of these particles on the current generation by using MFC. A constructed microbial fuel cell, which utilizes domestic wastewater, has been considered for wastewater treatment and bio-electricity generation. The two electrodes were connected to an external resistor (200 ohms). Experiments were conducted in two steps. First, the MFC was constructed without adding NZVI particles (Control) while at a second step, nanoparticles were added with a concentration of 50mg/L. After 20 hours, the measured voltage increased to 5 and 8mV, respectively. To conclude, the use of zero-valent iron in an MFC system can increase electricity generation.

Keywords: bacterial growth, electricity generation, microbial fuel cell MFC, nano zero-valent iron NZVI.

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Authors: Domenico Carmelo Mongelli

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The context is that of great uncertainty in the 27 countries belonging to the European Union which has adopted an epochal measure: the elimination of internal combustion engines for the traction of road vehicles starting from 2035 with complete replacement with electric vehicles. If on the one hand there is great concern at various levels for the unpreparedness for this change, on the other the Scientific Community is not preparing accurate studies on the problem, as the scientific literature deals with single aspects of the issue, moreover addressing the issue at the level of individual countries, losing sight of the global implications of the issue for the entire EU. The aim of the research is to fill these gaps: the technological, plant engineering, environmental, economic and employment aspects of the energy transition in question are addressed and connected to each other, comparing the current situation with the different scenarios that could exist in 2035 and in the following years until total disposal of the internal combustion engine vehicle fleet for the entire EU. The methodologies adopted by the research consist in the analysis of the entire life cycle of electric vehicles and batteries, through the use of specific databases, and in the dynamic simulation, using specific calculation codes, of the application of the results of this analysis to the entire EU electric vehicle fleet from 2035 onwards. Energy balance sheets will be drawn up (to evaluate the net energy saved), plant balance sheets (to determine the surplus demand for power and electrical energy required and the sizing of new plants from renewable sources to cover electricity needs), economic balance sheets (to determine the investment costs for this transition, the savings during the operation phase and the payback times of the initial investments), the environmental balances (with the different energy mix scenarios in anticipation of 2035, the reductions in CO2eq and the environmental effects are determined resulting from the increase in the production of lithium for batteries), the employment balances (it is estimated how many jobs will be lost and recovered in the reconversion of the automotive industry, related industries and in the refining, distribution and sale of petroleum products and how many will be products for technological innovation, the increase in demand for electricity, the construction and management of street electric columns). New algorithms for forecast optimization are developed, tested and validated. Compared to other published material, the research adds an overall picture of the energy transition, capturing the advantages and disadvantages of the different aspects, evaluating the entities and improvement solutions in an organic overall picture of the topic. The results achieved allow us to identify the strengths and weaknesses of the energy transition, to determine the possible solutions to mitigate these weaknesses and to simulate and then evaluate their effects, establishing the most suitable solutions to make this transition feasible.

Keywords: engines, Europe, mobility, transition

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Authors: Isara Muangthai, Lin Sue Jane

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Electricity is vital to every country’s economy in the world. For Thailand, the electricity generation sector plays an important role in the economic system, and it is the largest source of CO2 emissions. The aim of this paper is to use the decomposition analysis to investigate the key factors contributing to the changes of CO2 emissions from the electricity sector. The decomposition analysis has been widely used to identify and assess the contributors to the changes in emission trends. Our study adopted the Divisia index decomposition to identify the key factors affecting the evolution of CO2 emissions from Thailand’s thermal power sector during 2000-2011. The change of CO2 emissions were decomposed into five factors, including: Emission coefficient, heat rate, fuel intensity, electricity intensity, and economic growth. Results have shown that CO2 emission in Thailand’s thermal power sector increased 29,173 thousand tons during 2000-2011. Economic growth was found to be the primary factor for increasing CO2 emissions, while the electricity intensity played a dominant role in decreasing CO2 emissions. The increasing effect of economic growth was up to 55,924 million tons of CO2 emissions because the growth and development of the economy relied on a large electricity supply. On the other hand, the shifting of fuel structure towards a lower-carbon content resulted in CO2 emission decline. Since the CO2 emissions released from Thailand’s electricity generation are rapidly increasing, the Thailand government will be required to implement a CO2 reduction plan in the future. In order to cope with the impact of CO2 emissions related to the power sector and to achieve sustainable development, this study suggests that Thailand’s government should focus on restructuring the fuel supply in power generation towards low carbon fuels by promoting the use of renewable energy for electricity, improving the efficiency of electricity use by reducing electricity transmission and the distribution of line losses, implementing energy conservation strategies by enhancing the purchase of energy-saving products, substituting the new power plant technology in the old power plants, promoting a shift of economic structure towards less energy-intensive services and orienting Thailand’s power industry towards low carbon electricity generation.

Keywords: co2 emission, decomposition analysis, electricity generation, energy consumption

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Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

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This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

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Authors: Maha Hammami, Olfa Benouda Sioud

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This study investigates the reliability of management earnings forecasts with reference to these two ingredients: verifiability and neutrality. Specifically, we examine the biasedness (or accuracy) of management earnings forecasts and company specific characteristics that can be associated with accuracy. Based on sample of 102 IPO prospectuses published for admission on NYSE Euronext Paris from 2002 to 2010, we found that these forecasts are on average optimistic and two of the five test variables, earnings variability and financial leverage are significant in explaining ex post bias. Acknowledging the possibility that the bias is the result of the managers’ forecasting behavior, we then examine whether managers decide to under-predict, over-predict or forecast accurately for self-serving purposes. Explicitly, we examine the role of financial distress, operating performance, ownership by insiders and the economy state in influencing managers’ forecasting preferences. We find that managers of distressed firms seem to over-predict future earnings. We also find that when managers are given more stock options, they tend to under-predict future earnings. Finally, we conclude that the management earnings forecasts are affected by an intentional bias due to managers’ forecasting preferences.

Keywords: intentional bias, management earnings forecasts, neutrality, verifiability

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Authors: S. Forughi, A. De Zoysa, S. Bhati

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The present study focuses on the environmental performance of the companies in the electricity-producing sector and its relationship with their financial performance. We will review the major studies that examined the relationship between the environmental and financial performance of firms in various industries. While the classical economic debates consider the environmental friendly activities costly and harmful to a firm’s profitability, it is claimed that firms will be rewarded with higher profitability in long run through the investments in environmental friendly activities. In this context, prior studies have examined the relationship between the environmental and financial performance of firms operating in different industry sectors. Our study will employ an environmental indicator to increase the accuracy of the results and be employed as an independent variable in our developed econometric model to evaluate the impact of the financial performance of the firms on their environmental friendly activities in the context of companies operating in the Australian electricity-producing sector. As a result, we expect our methodology to contribute to the literature and the findings of the study will help us to provide recommendations and policy implications to the electricity producers.

Keywords: Australian electricity sector, efficiency measurement, environmental-financial performance interaction, environmental index

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Authors: Odinah Cuartero-Enteria, Aive Pecasales, Jhadly Philip Buniel, Christian Joy Vega, Shiela Estubo

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In the Philippines, Mindanao Island has the cheapest electricity because of the hydroelectric plants. Due to the rapid increase of the electricity consumption which the sources of electricity cannot support, it causes rotating brownout during summer season. This study investigated the household energy usage and practices in the rural areas of northern part of the Mindanao Island, Philippines. The questionnaire that includes the respondents’ profile and their common practices in energy consumptions was used as a tool in gathering the data. Several households were subjected to the survey. Results show energy consumption is not dependent on the profile of the respondents. It was observed that most of the families prefer to use energy saving methods of reducing electricity consumption. The main energy saving methods are unplugging unused home appliances, using of compact fluorescent bulb and energy-efficient gadgets, and using high electricity consumption appliances by schedule. Based on the results, the households in the rural areas know the practices of reducing electricity consumption. However, it is highly recommended that concern agencies should initiate information dissemination and strict implementation of well-formulated energy conservation practices all over the areas in Mindanao.

Keywords: Philippines, Mindanao island, rural areas, households, energy usages, practices

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Authors: Jiangyong Liu, Xiangxiang Xu, Bote Luo, Xiaoxue Luo, Jiang Zhu, Lingzhi Yi

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To address the problems of non-linearity and high randomness of the original power load sequence causing the degradation of power load forecasting accuracy, a short-term load forecasting method is proposed. The method is based on the Least Square Support Vector Machine optimized by an Improved Sparrow Search Algorithm combined with the Variational Mode Decomposition proposed in this paper. The application of the variational mode decomposition technique decomposes the raw power load data into a series of Intrinsic Mode Functions components, which can reduce the complexity and instability of the raw data while overcoming modal confounding; the proposed improved sparrow search algorithm can solve the problem of difficult selection of learning parameters in the least Square Support Vector Machine. Finally, through comparison experiments, the results show that the method can effectively improve prediction accuracy.

Keywords: load forecasting, variational mode decomposition, improved sparrow search algorithm, least square support vector machine

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Authors: Şakir Bingöl, Abdullah Emre Aydemir, Abdullah Saado, Ahmet Akıl, Elif Canbaz, Feyza Nur Bulgurcu, Gizem Uzun, Günsu Bilge Dal, Muhammedcan Pirinççi

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Smart contracts, which can be applied in many different areas, from financial applications to the internet of things, come to the fore with their security, low cost, and self-executing features. In this paper, it is focused on peer-to-peer (P2P) energy trading and the implementation of the smart contract on the Ethereum blockchain. It is assumed a microgrid consists of consumers and prosumers that can produce solar and wind energy. The proposed architecture is a system where the prosumer makes the purchase or sale request in the smart contract and the maximum price obtained through the distribution system operator (DSO) by forecasting. It is aimed to forecast the hourly maximum unit price of energy by using deep learning instead of a fixed pricing. In this way, it will make the system more reliable as there will be more dynamic and accurate pricing. For this purpose, Istanbul's energy generation, energy consumption and market clearing price data were used. The consistency of the available data and forecasting results is observed and discussed with graphs.

Keywords: energy trading smart contract, deep learning, microgrid, forecasting, Ethereum, peer to peer

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Authors: Hugo Manoel Olivera Da Silva, Ricardo Silva Thé Pontes

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In this work, is presented an analysis of the Brazilian regulation on human exposure to electromagnetic fields, which provides limits to electric fields, magnetic and electromagnetic fields. The regulations for the electricity sector was in charge of the Agência Nacional de Energia Elétrica-ANEEL, the Brazilian Electricity Regulatory Agency, that made it through the Normative Resolution Nº 398/2010, resulting in a series of obligations for the agents of the electricity sector, especially in the areas of generation, transmission, and distribution.

Keywords: adverse effects, electric energy, electric and magnetic fields, human health, regulation

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Authors: Xiang-Yao Zheng, Jen-Cheng Wang, Joe-Air Jiang

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Improving the line ampacity while using existing power grids is an important issue that electricity dispatchers are now facing. Using the information provided by the dynamic thermal rating (DTR) of transmission lines, an overhead power grid can operate safely. However, dispatchers usually lack real-time DTR information. Thus, this study proposes a long-short-term memory (LSTM)-based method, which is one of the neural network models. The LSTM-based method predicts the DTR of lines using the weather data provided by Central Weather Bureau (CWB) of Taiwan. The possible thermal bottlenecks at different locations along the line and the margin of line ampacity can be real-time determined by the proposed LSTM-based prediction method. A case study that targets the 345 kV power grid of TaiPower in Taiwan is utilized to examine the performance of the proposed method. The simulation results show that the proposed method is useful to provide the information for the smart grid application in the future.

Keywords: electricity dispatch, line ampacity prediction, dynamic thermal rating, long-short-term memory neural network, smart grid

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Authors: Berker Bayazit, Gulgun Kayakutlu

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The World Energy Outlook shows that energy markets will substantially change within a few forthcoming decades. First, determined action plans according to COP21 and aim of CO₂ emission reduction have already impact on policies of countries. Secondly, swiftly changed technological developments in the field of renewable energy will be influential upon medium and long-term energy generation and consumption behaviors of countries. Furthermore, share of electricity on global energy consumption is to be expected as high as 40 percent in 2040. Electrical vehicles, heat pumps, new electronical devices and digital improvements will be outstanding technologies and innovations will be the testimony of the market modifications. In order to meet highly increasing electricity demand caused by technologies, countries have to make new investments in the field of electricity production, transmission and distribution. Specifically, electricity generation mix becomes vital for both prevention of CO₂ emission and reduction of power prices. Majority of the research and development investments are made in the field of electricity generation. Hence, the prime source diversity and source planning of electricity generation are crucial for improving the wealth of citizen life. Approaches considering the CO₂ emission and total cost of generation, are necessary but not sufficient to evaluate and construct the product mix. On the other hand, employment and positive contribution to macroeconomic values are important factors that have to be taken into consideration. This study aims to constitute new investments in renewable energies (solar, wind, geothermal, biogas and hydropower) between 2018-2023 under 4 different goals. Therefore, a multi-objective programming model is proposed to optimize the goals of minimizing the CO₂ emission, investment amount and electricity sales price while maximizing the total employment and positive contribution to current deficit. In order to avoid the user preference among the goals, Dinkelbach’s algorithm and Guzel’s approach have been combined. The achievements are discussed with comparison to the current policies. Our study shows that new policies like huge capacity allotment might be discussible although obligation for local production is positive. The improvements in grid infrastructure and re-design support for the biogas and geothermal can be recommended.

Keywords: energy generation policies, multi-objective linear programming, portfolio planning, renewable energy

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Authors: R. Ramesh, K. K. Shivaraman

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The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

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Authors: M. J. Shofa, A. Hidayatno, O. M. Armand

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MRP as a production control system is appropriate for the deterministic environment. Unfortunately, most production systems such as customer demands are stochastic. Demand-Driven MRP (DDMRP) is a new approach for inventory control system, and it deals with demand uncertainty. The objective of this paper is to compare the MRP and DDMRP work for a long lead time and uncertain demand in terms of on-hand inventory levels. The evaluation is conducted through a discrete event simulation using purchased part data from an automotive company. The result is MRP gives 50,759 pcs / day while DDMRP gives 34,835 pcs / day (reduce 32%), it means DDMRP is more effective inventory control than MRP in terms of on-hand inventory levels.

Keywords: Demand-Driven MRP, long lead time, MRP, uncertain demand

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Authors: Roman Kalvin, Anam Nadeem, Saba Arif, Juntakan Taweekun

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In the current scenario demand of power is increasing day by day with increasing population. It is needed to sort out this problem with a technique which will not only overcome this energy crisis but also should be environment friendly. This project emphasizes on idea which shows that power could be generated by specially designed speed breaker. This project shows clearly how power can be generated by using Cam Mechanism where basically linear motion is converted into rotatory motion that can be used to generate electricity. When vehicle passes over the speed breaker, presses the cam with the help of connecting rod which rotate main shaft attached with large pulley. A flywheel is coupled with the shaft whose purpose is to normalize the oscillation in the energy and to make the energy unvarying. So, the shafts will spin with firm rpm. These shafts are coupled from end to end with a belt drive. The results show that power generated from this mechanism is 12 watts. The generated electricity does not required any fuel consumption it only generates power which can be used for the street light as well as for the traffic signals.

Keywords: revolution per minute, RPM, cam, speed breaker, rotatory motion

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Authors: Prima Fithri, Lusi Susanti, Karin Bestarina

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Indonesia's electrification ratio is still around 80.1%, which means that approximately 19.9% of households in Indonesia have not been getting the flow of electrical energy. Household electricity consumptions in Indonesia are generally still dominated by the public urban. In the city of Padang, West Sumatera, Indonesia, about 94.10% are power users of government services (PLN). The most important thing of the issue is human resources efficient energy. Consumer behavior in utilizing electricity becomes significant. Intensive questioner survey, in-depth interview and statistical analysis are carried out to collect scientific evidences of the behavioral based changes instruments to reduce electricity consumption in household sector. The questioner was developed to include five factors assuming affect the electricity consumption pattern in household sector. They are: attitude, energy price, household income, knowledge and other determinants. The survey was carried out in Padang, West Sumatra Province Indonesia. About 210 questioner papers were proportionally distributed to households in 11 districts in Padang. Stratified sampling was used as a method to select respondents. The results show that the household size, income, payment methods and size of house are factors affecting electricity saving behavior in residential sector. Household expenses on electricity are strongly influenced by gender, type of job, level of education, size of house, income, payment method and level of installed power. These results provide a scientific evidence for stakeholders on the potential of controlling electricity consumption and designing energy policy by government in residential sector.

Keywords: electricity, energy saving, household, behavior, policy

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Authors: Julian David Hunt, Andreas Nascimento

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Green hydrogen is the most environmental, renewable alternative to produce hydrogen. However, an important challenge to make hydrogen a competitive energy carrier is a constant supply of renewable energy, such as solar, wind and hydropower. Given that the electricity generation potential of these sources vary seasonally and interannually, this paper proposes installing an electrolysis hydrogen production plant in a ship and move the ship to the locations where electricity is cheap, or where the seasonal potential for renewable generation is high. An example of electrolysis ship application is to produce green hydrogen with hydropower from the North region of Brazil and then sail to the Northeast region of Brazil and generate hydrogen using excess electricity from offshore wind power. The electrolysis ship concept is interesting because it has the flexibility to produce green hydrogen using the cheapest renewable electricity available in the market.

Keywords: green hydrogen, electrolysis ship, renewable energies, seasonal variations

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Authors: Diteboho Xaba, Kolentino Mpeta, Tlotliso Qejoe

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This paper assesses the in-sample forecasting of the South African exchange rates comparing a linear ARIMA model and a SETAR model. The study uses a monthly adjusted data of South African exchange rates with 420 observations. Akaike information criterion (AIC) and the Schwarz information criteria (SIC) are used for model selection. Mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) are error metrics used to evaluate forecast capability of the models. The Diebold –Mariano (DM) test is employed in the study to check forecast accuracy in order to distinguish the forecasting performance between the two models (ARIMA and SETAR). The results indicate that both models perform well when modelling and forecasting the exchange rates, but SETAR seemed to outperform ARIMA.

Keywords: ARIMA, error metrices, model selection, SETAR

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Authors: Franco Ferrucci, Driss Stitou, Pascal Ortega, Franck Lucas

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The energy situation in tropical insular regions, as found in the French Polynesian islands, presents a number of challenges, such as high dependence on imported fuel, high transport costs from the mainland and weak electricity grids. Alternatively, these regions have a variety of renewable energy resources, which favor the exploitation of smart microgrids and energy storage technologies. With regards to the electrical energy demand, the high temperatures in these regions during the entire year implies that a large proportion of consumption is used for cooling buildings, even during the evening hours. In this context, this paper presents an air conditioning system driven by photovoltaic (PV) electricity that combines a refrigeration system and a thermochemical storage process. Thermochemical processes are able to store energy in the form of chemical potential with virtually no losses, and this energy can be used to produce cooling during the evening hours without the need to run a compressor (thus no electricity is required). Such storage processes implement thermochemical reactors in which a reversible chemical reaction between a solid compound and a gas takes place. The solid/gas pair used in this study is BaCl2 reacting with ammonia (NH3), which is also the coolant fluid in the refrigeration circuit. In the proposed system, the PV-driven electric compressor is used during the daytime either to run the refrigeration circuit when a cooling demand occurs or to decompose the ammonia-charged salt and remove the gas from thermochemical reactor when no cooling is needed. During the evening, when there is no electricity from solar source, the system changes its configuration and the reactor reabsorbs the ammonia gas from the evaporator and produces the cooling effect. In comparison to classical PV-driven air conditioning units equipped with electrochemical batteries (e.g. Pb, Li-ion), the proposed system has the advantage of having a novel storage technology with a much longer charge/discharge life cycle, and no self-discharge. It also allows a continuous operation of the electric compressor during the daytime, thus avoiding the problems associated with the on-off cycling. This work focuses on the system concept and on the efficiency study of its main components. It also compares the thermochemical with electrochemical storage as well as with other forms of thermal storage, such as latent (ice) and sensible heat (chilled water). The preliminary results show that the system seems to be a promising alternative to simultaneously fulfill cooling and energy storage needs in tropical insular regions.

Keywords: microgrid, solar air-conditioning, solid/gas sorption, thermochemical storage, tropical and insular regions

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Authors: Charline David, Alexandre Blondin Massé, Arnaud Zinflou

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In 2016, Clements, Hurn, and Li proposed a multiple equation time series approach for the short-term load forecasting, reporting an average mean absolute percentage error (MAPE) of 1.36% on an 11-years dataset for the Queensland region in Australia. We present an adaptation of their model to the electrical power load consumption for the whole Quebec province in Canada. More precisely, we take into account two additional meteorological variables — cloudiness and wind speed — on top of temperature, as well as the use of multiple meteorological measurements taken at different locations on the territory. We also consider other minor improvements. Our final model shows an average MAPE score of 1:79% over an 8-years dataset.

Keywords: short-term load forecasting, special days, time series, multiple equations, parallelization, clustering

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

Abstract:

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

Procedia PDF Downloads 267

Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

Abstract:

High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch

Procedia PDF Downloads 163

Authors: Kaiping Xu, Aiju You, Lei Hua

Abstract:

In view of the ecological environmental problems and protection needs of mountainous rivers in Zhejiang province, a suitable ecological water demand evaluation system was established based on investigation and monitoring. Taking the Kaihua river as an example, the research on ecological water demand and the current situation evaluation were carried out. The main types of ecological water demand in Majin River are basic ecological flow and lake wetland outside the river, and instream flow and water demands for water quality in Zhongcun river. In the wet season, each ecological water demand is 18.05m3/s and 2.56m3 / s, and in the dry season is 3.00m3/s and 0.61m3/s. Three indexes of flow, duration and occurrence time are used to evaluate the ecological water demand. The degree of ecological water demand in the past three years is low level of satisfaction. Meanwhile, the existing problems are analyzed, and put forward reasonable and operable safeguards and suggestions.

Keywords: Zhejiang province, mountainous river, ecological water demand, Kaihua river, evaluation

Procedia PDF Downloads 196

Authors: M. Erol Genevois, D. Celik, H. Z. Ulukan

Abstract:

Automated teller machines (ATMs) can be considered among one of the most important service facilities in the banking industry. The investment in ATMs and the impact on the banking industry is growing steadily in every part of the world. The banks take into consideration many factors like safety, convenience, visibility, cost in order to determine the optimum locations of ATMs. Today, ATMs are not only available in bank branches but also at retail locations. Another important factor is the cash management in ATMs. A cash demand model for every ATM is needed in order to have an efficient cash management system. This forecasting model is based on historical cash demand data which is highly related to the ATMs location. So, the location and the cash management problem should be considered together. Although the literature survey on facility location models is quite large, it is surprising that there are only few studies which handle together ATMs location and cash management problem. In order to fulfill the gap, this paper provides a general review on studies, efforts and development in ATMs location and cash management problem.

Keywords: ATM location problem, cash management problem, ATM cash replenishment problem, literature review in ATMs

Procedia PDF Downloads 454

Authors: Mehmet Savsar

Abstract:

One of the most important factors affecting power plant performance is the reliability of the turbine units operated under different conditions. Reliability directly affects plant availability and performance. Therefore, it is very important to be able to analyze turbine units, as well as power plant system reliability and availability under various operational conditions. In this paper, data related to power station failures are collected and analyzed in detail for all power stations in the state of Kuwait. Failures are characterized and categorized. Reliabilities of various power plants are analyzed and availabilities are quantified. Based on calculated availabilities of all installed power plants, actual power output is estimated. Furthermore, based on the past 15 years of data, power consumption trend is determined and the demand for power in the future is forecasted. Estimated power output is compared to the forecasted demand in order to determine the need for future capacity expansion.

Keywords: power plants, reliability, availability, capacity, preventive maintenance, forecasting

Procedia PDF Downloads 329