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Relevant bibliographies by topics / Demand forecasting in Nigeria

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Academic literature on the topic 'Demand forecasting in Nigeria'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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Journal articles on the topic "Demand forecasting in Nigeria"

1

Ayoade, J. O. "Forecasting and managing the demand for water in Nigeria." International Journal of Water Resources Development 3, no. 4 (1987): 222–27. http://dx.doi.org/10.1080/07900628708722353.

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Olaniyi, Adeniran, Adetayo, and Kanyio, Olufunto Adedotun. "Long Term Forecasting of International Air Travel Demand in Nigeria (2018-2028)." American International Journal of Multidisciplinary Scientific Research 1, no. 2 (2018): 16–24. http://dx.doi.org/10.46281/aijmsr.v1i2.184.

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This study examines long term forecasting of international air travel demand in Nigeria. Yearly data from 2001 to 2017 were collected from secondary sources. Ordinary Least Square (OLS) regression was used to forecast the ten years (2018 to 2028) demand for international air passenger travel in Nigeria. The demand for international air passenger in Nigeria from year 2001 to 2017 was compared with the forecast. Calculation reveals that the coefficient of determination R2 is 0.815, while the computed reveals that the coefficient of determination R2 is 0.769, this difference can be attributed to

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Sobowale, A., and K. S. Adeyemo. "Modeling water demand in a growing public university in Nigeria." Nigerian Journal of Technology 39, no. 4 (2021): 1255–62. http://dx.doi.org/10.4314/njt.v39i4.35.

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Nigerian university campuses are facing the twin problems of providing portable water of adequate quantity and quality and the sustainability of such supply. This paper examines the water demand status of a public University in Nigeria. Domestic, Public and Industrial water uses were considered while population forecasting was done using regression analysis for a 30 years design period (2018 – 2048). Results reveals possible population increase of 53.8 % by 2048 when the institution will clock 60 years. Water demand is also expected to rise sharply from 5,206 m3 day -1 (2018) to 10,959 m3 day

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Oyediran Oyelami, Benjamin, and Adedamola adedoyin Adewumi. "Models for Forecasting the Demand and Supply of Electricity in Nigeria." American Journal of Modeling and Optimization 2, no. 1 (2014): 25–33. http://dx.doi.org/10.12691/ajmo-2-1-4.

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Ogundari, I. O., A. S. Momodu, A. J. Famurewa, J. B. Akarakiri, and W. O. Siyanbola. "Analysis of Sustainable Cassava Biofuel Production in Nigeria." Energy & Environment 23, no. 4 (2012): 599–618. http://dx.doi.org/10.1260/0958-305x.23.4.599.

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Nigeria's biofuels policy advocates the adoption of cassava as feedstock for a 10%-biofuel substitution option in Nigerian transport fuel demand. This policy option is expected to address energy security and environmental consequences of using fossil fuels as the sole source of transport energy in the country. This paper appraised the technological and economic factors necessary for achieving Nigeria's cassava-based biofuel initiative at different substitution levels of 5, 10, and 15% by the Year 2020. A multi-stage energy forecasting and project analysis framework adapted from Coate's structu

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OLUFISAYO, ADE-IKUESAN OLANIKE, ATILOLA MORUFDEEN OLATUNBOSUN, OYEDEJI AJIBOLA OLUWAFEMI, and ADEYEMI HEZEKIAH OLUWOLE. "FUZZY LOGIC APPROACH TO ENERGY PLANNING IN NIGERIA." Journal of Engineering Studies and Research 26, no. 4 (2021): 86–96. http://dx.doi.org/10.29081/jesr.v26i4.240.

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Energy planning is an important tool for power system utility company and consumer’s profitability and satisfaction respectively. This paper is a study of energy planning (forecasting) in Ogun state of Nigeria using Fuzzy Logic model. Population and gross domestic product (GDP) are used as the independent variables to forecast load demand based on the previous load demand. After arranging the variables into 5 membership functions and the 19 rules were created, the fuzzy logic model forecast the annual load demand for the next 10 years with a percentage error margin 0.95 % to 21.79 % which resu

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Okakwu, I. K., E. S. Oluwasogo, A. E. Ibhaze, and A. L. Imoize. "A comparative study of time series analysis for forecasting energy demand in Nigeria." Nigerian Journal of Technology 38, no. 2 (2019): 465. http://dx.doi.org/10.4314/njt.v38i2.24.

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Musa, Bashir, Nasser Yimen, Sani Isah Abba, Humphrey Hugh Adun, and Mustafa Dagbasi. "Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach." Processes 9, no. 7 (2021): 1166. http://dx.doi.org/10.3390/pr9071166.

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The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coeff

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Habeeb, Munirat, Umar Mohammed, and Ekechukwu Henry. "EFFECTS OF THE MANAGEMENT OF FINISHED GOODS INVENTORY ON THE SALES VOLUME OF 7UP BOTTLING COMPANY, NORTH CENTRAL NIGERIA." International Journal of Innovative Research in Social Sciences & Strategic Management Techniques 8, no. 1 (2021): 70–82. http://dx.doi.org/10.48028/iiprds/ijirsssmt.v8.i1.06.

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The challenge of keeping finished goods inventory at optimum levels has been a major issue that has affected the sales volume of 7UP Bottling Company Plc. This study examined the effects of the management of finished goods inventory and the sales volume of 7UP Bottling Company in North Central, Nigeria. The study examined how forecasting demand, product handling as well product planning and warehouse management impact on the sales volume of7UP Bottling Company in North-Central Nigeria. The study adopted a combination of survey, explanatory and exploratory research, which involved the use of pr

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Jones, Rod. "Forecasting demand." British Journal of Healthcare Management 16, no. 8 (2010): 392–93. http://dx.doi.org/10.12968/bjhc.2010.16.8.77654.

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Dissertations / Theses on the topic "Demand forecasting in Nigeria"

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Martin, C. A. "International tourism demand forecasting." Thesis, University of Bradford, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379816.

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Syntetos, Argyrios. "Forecasting of intermittent demand." Thesis, Online version, 2001. http://bibpurl.oclc.org/web/26215.

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Gato, Shirley, and s3024038@rmit edu au. "Forecasting Urban Residential Water Demand." RMIT University. Civil, Environmental and Chemical Engineering, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070202.113452.

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The city of Melbourne in Victoria, Australia has been recognised as having high quality drinking water, but like other urban cities in the world, its growing population means increasing water demand. Melbourne is also already on its eight year of dry climatic conditions and is currently experiencing a drought that forced water authorities to impose water restrictions after 20 years of unrestricted supply. The current drought, dwindling supplies and possible impact of climate change highlight the importance of making better use of this precious resource. The Water Resources Strate

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Rostami, Tabar Bahman. "ARIMA demand forecasting by aggregation." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00980614.

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Demand forecasting performance is subject to the uncertainty underlying the time series an organisation is dealing with. There are many approaches that may be used to reduce demand uncertainty and consequently improve the forecasting (and inventory control) performance. An intuitively appealing such approach that is known to be effective is demand aggregation. One approach is to aggregate demand in lower-frequency 'time buckets'. Such an approach is often referred to, in the academic literature, as temporal aggregation. Another approach discussed in the literature is that associated with cross

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GOMES, RENATA MIRANDA. "BIAS DETECTION IN DEMAND FORECASTING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=18477@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO<br>Essa dissertação teve como objetivo propor dois novos métodos para detecção de viés na previsão de demanda. Os métodos consistem numa adaptação de duas técnicas de controle estatístico de processos, o gráfico de controle de EWMA e o algoritmo CUSUM, ao contexto de detecção de viés na previsão de demanda. O desempenho dos métodos foi analisado por simulação, para diversos casos de mudança na inclinação (tendência) da série de dados (mudança de modelo constante para modelo c

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Holbrook, Blair Sato. "Point-of-sale demand forecasting." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104397.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.<br>Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2016. In conjunction with the Leaders for Global Operations Program at MIT.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 38).<br>Nike Always Available (AA) is a significant global business unit within Nike that allows retail customers to purchase athletic

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Al-Madfai, Hasan. "Weather corrected electricity demand forecasting." Thesis, University of South Wales, 2002. https://pure.southwales.ac.uk/en/studentthesis/weather-corrected-electricity-demand-forecasting(2e066cc4-58b1-4694-9937-ee8f57fbed02).html.

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Electricity load forecasts now form an essential part of the routine operations of electricity companies. The complexity of the short-term load forecasting (STLF) problem arises from the multiple seasonal components, the change in consumer behaviour during holiday seasons and other social and religious events that affect electricity consumption. The aim of this research is to produce models for electricity demand that can be used to further the understanding of the dynamics of electricity consumption in South Wales. These models can also be used to produce weather corrected forecasts, and to p

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Ho, Kien K. (Kine Kit). "Demand forecasting for aircraft engine aftermarket." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43833.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2008.<br>Includes bibliographical references.<br>In 2006, Pratt and Whitney launched the Global Material Solutions business model aiming to supply spare parts to non-OEM engines with minimum 95% on-time delivery and fill-rate. Lacking essential technical knowledge of the target engines, predictability and associated confidence of the parts demands ar

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Ahmed, Shadman. "Phase-Out Demand Forecasting : Predictive modeling on forecasting product life cycle." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287446.

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The phase-out stage in a product life cycle can face unpredictable demand. Accurate forecast of the phase-out demand can help supply chain managers to control the number of obsolete inventories. Consequently, having a positive effect in terms of resources and lower scrap costs. In this thesis, we investigated if data-driven forecasting models could improve the accuracy of forecasting the phase-out stage when compared with domain experts. Since the space of available models is vast, a set of 11 best performing models according to literature were investigated. Furthermore, a thorough model selec

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Lawson, Richard. "Adaptive state-space forecasting of gas demand." Thesis, Lancaster University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358799.

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Books on the topic "Demand forecasting in Nigeria"

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Oni, Bankole. Employment prospects of informal sector vocational trainees in Southwestern Nigeria: The case of auto-technicians. Nigerian Institute of Social and Economic Research, 1999.

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Chase, Charles. Demand-Driven Forecasting. John Wiley & Sons, Ltd., 2009.

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Baker, Bill. Demand forecasting methodology. UK Water Industry Research Limited, 1995.

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Chase, Charles, ed. Demand-Driven Forecasting. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119203612.

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Chase, Charles W. Demand-Driven Forecasting. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118691861.

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Coulbeck, B. Water demand forecasting. Leicester Polytechnic, 1985.

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Hong, Wei-Chiang. Intelligent Energy Demand Forecasting. Springer London, 2013.

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Billings, R. Bruce. Forecasting urban water demand. 2nd ed. American Water Works Association, Science and Technology, 2007.

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9

Vaughan, Jones Clive, ed. Forecasting urban water demand. American Water Works Association, 1996.

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Hong, Wei-Chiang. Intelligent Energy Demand Forecasting. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4968-2.

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Book chapters on the topic "Demand forecasting in Nigeria"

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Ivanov, Dmitry, Alexander Tsipoulanidis, and Jörn Schönberger. "Demand Forecasting." In Springer Texts in Business and Economics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94313-8_11.

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Sene, Kevin. "Demand Forecasting." In Hydrometeorology. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3403-8_5.

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Doganis, Rigas. "Forecasting demand." In Flying Off Course. Routledge, 2019. http://dx.doi.org/10.4324/9781315402987-10.

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Wild, Tony. "Forecasting demand." In Best Practice in Inventory Management. Routledge, 2017. http://dx.doi.org/10.4324/9781315231532-12.

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Ivanov, Dmitry, Alexander Tsipoulanidis, and Jörn Schönberger. "Demand Forecasting." In Springer Texts in Business and Economics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24217-0_11.

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Sene, Kevin. "Demand Forecasting." In Hydrometeorology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23546-2_6.

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Mauergauz, Yuri. "Demand Forecasting." In Advanced Planning and Scheduling in Manufacturing and Supply Chains. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27523-9_6.

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Zhou, Dadi, Shixian Gao, Edgard Gnansounou, and Jun Dong. "Demand Forecasting." In Integrated Assessment of Sustainable Energy Systems in China The China Energy Technology Program. Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0153-3_4.

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Bhattacharyya, Subhes C. "Energy Demand Forecasting." In Energy Economics. Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7468-4_5.

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Renzetti, Steven. "Water Demand Forecasting." In The Economics of Water Demands. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0865-6_11.

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Conference papers on the topic "Demand forecasting in Nigeria"

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Sinn, Mathieu. "Energy demand forecasting." In e-Energy '14: The Fifth International Conference on Future Energy Systems. ACM, 2014. http://dx.doi.org/10.1145/2602044.2602086.

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Tang, Zhongjun, and Jing Xiao. "Tools for a New Demand Forecasting Paradigm "Individual Demand Forecasting'." In 2009 International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2009. http://dx.doi.org/10.1109/bife.2009.76.

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Tang, Zhongjun. "Mechanism for a New Demand Forecasting Paradigm 'Individual Demand Forecasting'." In 2010 3rd International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2010. http://dx.doi.org/10.1109/bife.2010.33.

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Garrow, Laurie A., Mohammad Ilbeigi, and Ziran Chen. "Forecasting Demand for On Demand Mobility." In 17th AIAA Aviation Technology, Integration, and Operations Conference. American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-3280.

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Meng, Xiangang, Hongsen Yan, and Yufang Wang. "Product Demand Forecasting in Knowledgeable Manufacturing Based on Demand Forecasting Net." In 2009 First International Workshop on Database Technology and Applications, DBTA. IEEE, 2009. http://dx.doi.org/10.1109/dbta.2009.120.

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Filippov, Sergey P., Natalia A. Grygorieva, and Elena M. Makarova. "Energy Demand Forecasting System." In 2018 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). IEEE, 2018. http://dx.doi.org/10.1109/fareastcon.2018.8602786.

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Veit, Andreas, Christoph Goebel, Rohit Tidke, Christoph Doblander, and Hans-Arno Jacobsen. "Household electricity demand forecasting." In e-Energy '14: The Fifth International Conference on Future Energy Systems. ACM, 2014. http://dx.doi.org/10.1145/2602044.2602082.

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Wheeler, David R., and Charles Shelley. "Forecasting Artificial Intelligence Demand." In 1986 Technical Symposium Southeast, edited by John F. Gilmore. SPIE, 1986. http://dx.doi.org/10.1117/12.964184.

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Chen, Hu, and Hua He. "Reverse Logistics Demand Forecasting under Demand Uncertainty." In International Conference of Logistics Engineering and Management (ICLEM) 2010. American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41139(387)49.

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Zhongjun Tang and Xiaohong Chen. "A new demand forecasting paradigm ‘customer-centric individual demand forecasting’." In 2007 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2007. http://dx.doi.org/10.1109/ieem.2007.4419370.

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Reports on the topic "Demand forecasting in Nigeria"

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Goodwill, Jay. Forecasting Paratransit Services Demand – Review and Recommendations. University of South Florida, 2013. http://dx.doi.org/10.5038/cutr-nctr-rr-2011-06.

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Pathak, Parag, and Peng Shi. Demand Modeling, Forecasting, and Counterfactuals, Part I. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w19859.

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Kiflu, Mordocai, and Carlos Lopez. Demand Forecasting: DLA'S Aviation Supply Chain High Value Products. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada626751.

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Langstroth, IV, and Theodore A. Forecasting Demand for KC-135 Sorties: Deploy to Dwell Impacts. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada587364.

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Orchowsky, Stan, Ronald Kirchoff, Janet Rider, and Dale Kem. A Study of Demand Forecasting in the Defense Logistics Agency. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada169637.

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Hersey, J. M., James M. Rowlett, and Shannon P. Thompson. Forecasting the Demand of the F414-GE-400 Engine at NAS Lemoore. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada493918.

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Bartels, P., H. Rijgersberg, J. Groot, H. Bos-Brouwers, and B. van Gogh. Future Food Basket : methodology for the forecasting of the future food demand. Wageningen Food & Biobased Research, 2018. http://dx.doi.org/10.18174/456661.

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Ueoka, Alan K. Describing Access and Forecasting Demand for Family Practice Services in the 121st General Hospital. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada421286.

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Takeshima, Hiroyuki, and Yanyan Liu. The roles of agroclimatic similarity and returns on scale in the demand for mechanization: Insights from Nigeria. International Food Policy Research Institute, 2019. http://dx.doi.org/10.2499/p15738coll2.133386.

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Sapp, James. Electricity Demand Forecasting in a Changing Regional Context: The Application of the Multiple Perspective Concept to the Prediction Process. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.574.

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