IDEAS home Printed from https://ideas.repec.org/e/pmc56.html
   My authors  Follow this author

Patrick Eugene McSharry

Personal Details

First Name:Patrick
Middle Name:Eugene
Last Name:McSharry
Suffix:
RePEc Short-ID:pmc56
http://www.mcsharry.net

Affiliation

(in no particular order)

University of Oxford, Department of Engineering Science

http://www.eng.ox.ac.uk/
UK, Oxford

University of Oxford, Mathematical Institute

http://www.maths.ox.ac.uk
Oxford, UK

Research output

as
Jump to: Articles

Articles

  1. Njuguna, Christopher & McSharry, Patrick, 2017. "Constructing spatiotemporal poverty indices from big data," Journal of Business Research, Elsevier, vol. 70(C), pages 318-327.
  2. Georgios Anastasiades & Patrick McSharry, 2013. "Quantile Forecasting of Wind Power Using Variability Indices," Energies, MDPI, vol. 6(2), pages 1-34, February.
  3. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.
  4. Patrick McSharry, 2012. "Stream Analytics for Forecasting," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 24, pages 7-12, Winter.
  5. McSharry, Patrick E., 2011. "Validation and forecasting accuracy in models of climate change: Comments," International Journal of Forecasting, Elsevier, vol. 27(4), pages 996-999, October.
  6. David Orrell & Patrick McSharry, 2009. "Reply to Commentaries," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 14, pages 1-39, Summer.
  7. David Orrell & Patrick McSharry, 2009. "A Systems Approach to Forecasting," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 14, pages 25-30, Summer.
  8. Orrell, David & McSharry, Patrick, 2009. "System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach," International Journal of Forecasting, Elsevier, vol. 25(4), pages 734-743, October.
  9. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Njuguna, Christopher & McSharry, Patrick, 2017. "Constructing spatiotemporal poverty indices from big data," Journal of Business Research, Elsevier, vol. 70(C), pages 318-327.

    Cited by:

    1. Ola Hall & Mattias Ohlsson & Thortseinn Rognvaldsson, 2022. "Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain," Papers 2203.01068, arXiv.org.
    2. Dedy Rahman Wijaya & Ni Luh Putu Satyaning Pradnya Paramita & Ana Uluwiyah & Muhammad Rheza & Annisa Zahara & Dwi Rani Puspita, 2022. "Estimating city-level poverty rate based on e-commerce data with machine learning," Electronic Commerce Research, Springer, vol. 22(1), pages 195-221, March.
    3. Boto Ferreira, Mário & Costa Pinto, Diego & Maurer Herter, Márcia & Soro, Jerônimo & Vanneschi, Leonardo & Castelli, Mauro & Peres, Fernando, 2021. "Using artificial intelligence to overcome over-indebtedness and fight poverty," Journal of Business Research, Elsevier, vol. 131(C), pages 411-425.
    4. Simone Cecchini & Giovanni Savio & Varinia Tromben, 2022. "Mapping poverty rates in Chile with night lights and fractional multinomial models," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(4), pages 850-876, August.
    5. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    6. McBride, Linden & Barrett, Christopher B. & Browne, Christopher & Hu, Leiqiu & Liu, Yanyan & Matteson, David S. & Sun, Ying & Wen, Jiaming, 2021. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," 2021 Allied Social Sciences Association (ASSA) Annual Meeting (Virtual), January 3-5, 2021, San Diego, California 309060, Agricultural and Applied Economics Association.
    7. McSharry, Patrick & Mawejje, Joseph, 2024. "Estimating urban GDP growth using nighttime lights and machine learning techniques in data poor environments: The case of South Sudan," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    8. Ola Hall & Francis Dompae & Ibrahim Wahab & Fred Mawunyo Dzanku, 2023. "A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications," Journal of International Development, John Wiley & Sons, Ltd., vol. 35(7), pages 1753-1768, October.
    9. Jessica E. Steele & Carla Pezzulo & Maximilian Albert & Christopher J. Brooks & Elisabeth zu Erbach-Schoenberg & Siobhán B. O’Connor & Pål R. Sundsøy & Kenth Engø-Monsen & Kristine Nilsen & Bonita Gra, 2021. "Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.
    10. Simon Lange & Utz Johann Pape & Peter Pütz, 2022. "Small Area Estimation of Poverty Under Structural Change," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S2), pages 264-281, December.
    11. Gregorio Izquierdo Llanes & Antonio Salcedo Galiano, 2023. "Why does equivalization matter? An application to the monetary poverty in the sustainable development goals framework," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2575-2589, June.
    12. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    13. El-Haddadeh, Ramzi & Osmani, Mohamad & Hindi, Nitham & Fadlalla, Adam, 2021. "Value creation for realising the sustainable development goals: Fostering organisational adoption of big data analytics," Journal of Business Research, Elsevier, vol. 131(C), pages 402-410.
    14. Akyildirim, Erdinc & Sensoy, Ahmet & Gulay, Guzhan & Corbet, Shaen & Salari, Hajar Novin, 2021. "Big data analytics, order imbalance and the predictability of stock returns," Journal of Multinational Financial Management, Elsevier, vol. 62(C).
    15. Darrold Cordes & Shahram Latifi & Gregory M. Morrison, 2022. "Systematic literature review of the performance characteristics of Chebyshev polynomials in machine learning applications for economic forecasting in low-income communities in sub-Saharan Africa," SN Business & Economics, Springer, vol. 2(12), pages 1-33, December.
    16. Yongming Xu & Yaping Mo & Shanyou Zhu, 2021. "Poverty Mapping in the Dian-Gui-Qian Contiguous Extremely Poor Area of Southwest China Based on Multi-Source Geospatial Data," Sustainability, MDPI, vol. 13(16), pages 1-14, August.

  2. Georgios Anastasiades & Patrick McSharry, 2013. "Quantile Forecasting of Wind Power Using Variability Indices," Energies, MDPI, vol. 6(2), pages 1-34, February.

    Cited by:

    1. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    2. Ricardo J. Bessa & Corinna Möhrlen & Vanessa Fundel & Malte Siefert & Jethro Browell & Sebastian Haglund El Gaidi & Bri-Mathias Hodge & Umit Cali & George Kariniotakis, 2017. "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry," Energies, MDPI, vol. 10(9), pages 1-48, September.
    3. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
    4. Ying-Yi Hong & Ti-Hsuan Yu & Ching-Yun Liu, 2013. "Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode Decomposition," Energies, MDPI, vol. 6(12), pages 1-16, November.
    5. Gallego-Castillo, Cristobal & Bessa, Ricardo & Cavalcante, Laura & Lopez-Garcia, Oscar, 2016. "On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power," Energy, Elsevier, vol. 113(C), pages 355-365.
    6. Arrieta-Prieto, Mario & Schell, Kristen R., 2022. "Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model," International Journal of Forecasting, Elsevier, vol. 38(1), pages 300-320.

  3. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.

    Cited by:

    1. Berg Tim Oliver, 2017. "Forecast accuracy of a BVAR under alternative specifications of the zero lower bound," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(2), pages 1-29, April.
    2. Radek DOSKOČIL & Karel DOUBRAVSKÝ, 2017. "Qualitative Evaluation of Knowledge Based Model of Project Time-Cost as Decision Making Support," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(1), pages 263-280.
    3. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    4. Mehmet Balcilar & Rangan Gupta & Renee van Eyden & Kirsten Thompson, 2015. "Comparing the Forecasting Ability of Financial Conditions Indices: The Case of South Africa," Working Papers 15-06, Eastern Mediterranean University, Department of Economics.
    5. Xiuying Ma & Yongjing Wang & Haiyan Song & Han Liu, 2020. "Time-varying mechanisms between foreign direct investment and tourism development under the new normal in China," Tourism Economics, , vol. 26(2), pages 324-343, March.
    6. Lan Bai & Xiafei Li & Yu Wei & Guiwu Wei, 2022. "Does crude oil futures price really help to predict spot oil price? New evidence from density forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3694-3712, July.
    7. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2015. "Was the recent downturn in US real GDP predictable?," Applied Economics, Taylor & Francis Journals, vol. 47(28), pages 2985-3007, June.
    8. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8, Bank for International Settlements.
    9. Grabowski Daniel & Staszewska-Bystrova Anna & Winker Peter, 2017. "Generating prediction bands for path forecasts from SETAR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-18, December.
    10. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 201230, University of Pretoria, Department of Economics.

  4. McSharry, Patrick E., 2011. "Validation and forecasting accuracy in models of climate change: Comments," International Journal of Forecasting, Elsevier, vol. 27(4), pages 996-999, October.

    Cited by:

    1. Hassani, Hossein & Silva, Emmanuel Sirimal & Gupta, Rangan & Das, Sonali, 2018. "Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 121-139.

  5. Orrell, David & McSharry, Patrick, 2009. "System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach," International Journal of Forecasting, Elsevier, vol. 25(4), pages 734-743, October.

    Cited by:

    1. Makridakis, Spyros & Taleb, Nassim, 2009. "Decision making and planning under low levels of predictability," International Journal of Forecasting, Elsevier, vol. 25(4), pages 716-733, October.
    2. -, 2011. "An assessment of the economic impact of climate change on the tourism sector In Barbados," Sede Subregional de la CEPAL para el Caribe (Estudios e Investigaciones) 38602, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    3. Dohnal, Mirko, 2016. "Complex biofuels related scenarios generated by qualitative reasoning under severe information shortages: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 676-684.
    4. Doubravsky, Karel & Dohnal, Mirko, 2018. "Qualitative equationless macroeconomic models as generators of all possible forecasts based on three trend values—Increasing, constant, decreasing," Structural Change and Economic Dynamics, Elsevier, vol. 45(C), pages 30-36.
    5. Zanoli, Raffaele & Gambelli, Danilo & Vairo, Daniela, 2012. "Scenarios of the organic food market in Europe," Food Policy, Elsevier, vol. 37(1), pages 41-57.
    6. Prasad, Ravita D. & Bansal, R.C. & Raturi, Atul, 2014. "Multi-faceted energy planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 686-699.
    7. Roberto Savona & Marika Vezzoli, 2015. "Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 66-92, February.
    8. Peter Nielsen & Liping Jiang & Niels Gorm Malý Rytter & Gang Chen, 2014. "An investigation of forecast horizon and observation fit's influence on an econometric rate forecast model in the liner shipping industry," Maritime Policy & Management, Taylor & Francis Journals, vol. 41(7), pages 667-682, December.
    9. Arvydas Jadevicius & Brian Sloan & Andrew Brown, 2013. "Property Market Modelling and Forecasting: A Case for Simplicity," ERES eres2013_10, European Real Estate Society (ERES).
    10. David Blockley, 2023. "Exchanging Obligations: Accounting for All Forms of Capital," Journal of Interdisciplinary Economics, , vol. 35(1), pages 7-28, January.
    11. Wright, George & Goodwin, Paul, 2009. "Decision making and planning under low levels of predictability: Enhancing the scenario method," International Journal of Forecasting, Elsevier, vol. 25(4), pages 813-825, October.
    12. Olga Kiuila, 2011. "Interactions between trade and environmental policies in the Czech economy," Working Papers 2011-16, Faculty of Economic Sciences, University of Warsaw.
    13. Derbyshire, James & Wright, George, 2017. "Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation," International Journal of Forecasting, Elsevier, vol. 33(1), pages 254-266.
    14. Jan Kwakkel & Gönenç Yücel, 2014. "An Exploratory Analysis of the Dutch Electricity System in Transition," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 5(4), pages 670-685, December.
    15. David Orrell, 2017. "A Quantum Theory of Money and Value, Part 2: The Uncertainty Principle," Economic Thought, World Economics Association, vol. 6(2), pages 14-26, September.
    16. Makridakis, Spyros & Hogarth, Robin M. & Gaba, Anil, 2009. "Forecasting and uncertainty in the economic and business world," International Journal of Forecasting, Elsevier, vol. 25(4), pages 794-812, October.
    17. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.

  6. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.

    Cited by:

    1. D J Pedregal & P C Young, 2008. "Development of improved adaptive approaches to electricity demand forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(8), pages 1066-1076, August.
    2. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    3. Reisen, Valdério A. & Zamprogno, Bartolomeu & Palma, Wilfredo & Arteche, Josu, 2014. "A semiparametric approach to estimate two seasonal fractional parameters in the SARFIMA model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 98(C), pages 1-17.
    4. Tanrisever, Fehmi & Derinkuyu, Kursad & Heeren, Michael, 2013. "Forecasting electricity infeed for distribution system networks: An analysis of the Dutch case," Energy, Elsevier, vol. 58(C), pages 247-257.
    5. Eran Raviv & Kees E. Bouwman & Dick van Dijk, 2013. "Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices," Tinbergen Institute Discussion Papers 13-068/III, Tinbergen Institute.
    6. Pielow, Amy & Sioshansi, Ramteen & Roberts, Matthew C., 2012. "Modeling short-run electricity demand with long-term growth rates and consumer price elasticity in commercial and industrial sectors," Energy, Elsevier, vol. 46(1), pages 533-540.
    7. Taylor, James W., 2006. "Density forecasting for the efficient balancing of the generation and consumption of electricity," International Journal of Forecasting, Elsevier, vol. 22(4), pages 707-724.
    8. Alexios Lekidis & Elpiniki I. Papageorgiou, 2023. "Edge-Based Short-Term Energy Demand Prediction," Energies, MDPI, vol. 16(14), pages 1-20, July.
    9. Sulandari, Winita & Subanar, & Lee, Muhammad Hisyam & Rodrigues, Paulo Canas, 2020. "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks," Energy, Elsevier, vol. 190(C).
    10. -, 2011. "An assessment of the economic impact of climate change on the tourism sector In Barbados," Sede Subregional de la CEPAL para el Caribe (Estudios e Investigaciones) 38602, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    11. Eichler, M. & Grothe, O. & Manner, H. & Türk, D.D.T., 2012. "Modeling spike occurrences in electricity spot prices for forecasting," Research Memorandum 029, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    12. Kohler, M. & Blond, N. & Clappier, A., 2016. "A city scale degree-day method to assess building space heating energy demands in Strasbourg Eurometropolis (France)," Applied Energy, Elsevier, vol. 184(C), pages 40-54.
    13. Chan, Kam Fong & Gray, Philip & van Campen, Bart, 2008. "A new approach to characterizing and forecasting electricity price volatility," International Journal of Forecasting, Elsevier, vol. 24(4), pages 728-743.
    14. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    15. Joan Batalla-Bejerano & Maria Teresa Costa-Campi & Elisa Trujillo-Baute, 2015. "Unexpected consequences of liberalisation: metering, losses, load profiles and cost settlement in Spain’s electricity system," Working Papers 2015/16, Institut d'Economia de Barcelona (IEB).
    16. Taylor, James W., 2010. "Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles," International Journal of Forecasting, Elsevier, vol. 26(4), pages 627-646, October.
    17. Zhou, Fan & Page, Lionel & Perrons, Robert K. & Zheng, Zuduo & Washington, Simon, 2019. "Long-term forecasts for energy commodities price: What the experts think," Energy Economics, Elsevier, vol. 84(C).
    18. Magnano, L. & Boland, J.W., 2007. "Generation of synthetic sequences of electricity demand: Application in South Australia," Energy, Elsevier, vol. 32(11), pages 2230-2243.
    19. Liang, Xin & Hong, Tianzhen & Shen, Geoffrey Qiping, 2016. "Improving the accuracy of energy baseline models for commercial buildings with occupancy data," Applied Energy, Elsevier, vol. 179(C), pages 247-260.
    20. Paraschiv, Florentina & Erni, David & Pietsch, Ralf, 2014. "The impact of renewable energies on EEX day-ahead electricity prices," Energy Policy, Elsevier, vol. 73(C), pages 196-210.
    21. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    22. Cristina Miranda & Reinaldo Castro Souza & Mônica Barros & Cristina Vidigal Cabral de Miranda, 2007. "Short Term Demand Forecasting Using Double Exponential Smoothing and Interventions to Account for Holidays and Temperature Effects," EcoMod2007 23900058, EcoMod.
    23. Miloš Božić & Miloš Stojanović & Zoran Stajić & Dragan Tasić, 2013. "A New Two-Stage Approach to Short Term Electrical Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-19, April.
    24. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    25. Silva, Hendrigo Batista da & Santiago, Leonardo P., 2018. "On the trade-off between real-time pricing and the social acceptability costs of demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1513-1521.
    26. Tine Van Calster & Filip Van den Bossche & Bart Baesens & Wilfried Lemahieu, 2020. "Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective," Papers 2002.00949, arXiv.org.
    27. Taylor, James W., 2008. "An evaluation of methods for very short-term load forecasting using minute-by-minute British data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 645-658.
    28. Cancelo, José Ramón & Grafe, Rosmarie, 2007. "Forecasting from one day to one week ahead for the Spanish system operator," DES - Working Papers. Statistics and Econometrics. WS ws078418, Universidad Carlos III de Madrid. Departamento de Estadística.
    29. Rendon-Sanchez, Juan F. & de Menezes, Lilian M., 2019. "Structural combination of seasonal exponential smoothing forecasts applied to load forecasting," European Journal of Operational Research, Elsevier, vol. 275(3), pages 916-924.
    30. Smith, Michael Stanley & Shively, Thomas S., 2018. "Econometric modeling of regional electricity spot prices in the Australian market," Energy Economics, Elsevier, vol. 74(C), pages 886-903.
    31. Ayman A. Amin, 2020. "Bayesian Analysis of Double Seasonal Autoregressive Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 328-352, November.
    32. Niematallah Elamin & Mototsugu Fukushige, 2017. "The 2011 Japanese energy crisis: Effects on the magnitude and pattern of load demand," Discussion Papers in Economics and Business 17-19, Osaka University, Graduate School of Economics.
    33. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2019. "Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting," Energies, MDPI, vol. 12(1), pages 1-21, January.
    34. Taylor, James W. & Snyder, Ralph D., 2012. "Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing," Omega, Elsevier, vol. 40(6), pages 748-757.
    35. Ismail Shah & Francesco Lisi, 2020. "Forecasting of electricity price through a functional prediction of sale and purchase curves," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 242-259, March.
    36. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
    37. Bassamzadeh, Nastaran & Ghanem, Roger, 2017. "Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks," Applied Energy, Elsevier, vol. 193(C), pages 369-380.
    38. Chethana Dharmawardane & Ville Sillanpää & Jan Holmström, 2021. "High-frequency forecasting for grocery point-of-sales: intervention in practice and theoretical implications for operational design," Operations Management Research, Springer, vol. 14(1), pages 38-60, June.
    39. Kim, Myung Suk, 2013. "Modeling special-day effects for forecasting intraday electricity demand," European Journal of Operational Research, Elsevier, vol. 230(1), pages 170-180.
    40. Fezzi, Carlo & Fanghella, Valeria, 2021. "Tracking GDP in real-time using electricity market data: Insights from the first wave of COVID-19 across Europe," European Economic Review, Elsevier, vol. 139(C).
    41. Leonard Burg & Gonca Gürses-Tran & Reinhard Madlener & Antonello Monti, 2021. "Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels," Energies, MDPI, vol. 14(21), pages 1-16, November.
    42. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    43. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
    44. Safiullah, Hameed, 2011. "Evaluation of Grid Level Impacts of Electric Vehicles," MPRA Paper 59175, University Library of Munich, Germany.
    45. Kamal Chapagain & Somsak Kittipiyakul & Pisut Kulthanavit, 2020. "Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand," Energies, MDPI, vol. 13(10), pages 1-29, May.
    46. Crozier, Constance & Apostolopoulou, Dimitra & McCulloch, Malcolm, 2018. "Mitigating the impact of personal vehicle electrification: A power generation perspective," Energy Policy, Elsevier, vol. 118(C), pages 474-481.
    47. Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008. "An hourly periodic state space model for modelling French national electricity load," International Journal of Forecasting, Elsevier, vol. 24(4), pages 566-587.
    48. Rotger, G.P. & Franses, Ph.H.B.F., 2006. "Forecasting high-frequency electricity demand with a diffusion index model," Econometric Institute Research Papers EI 2006-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    49. María Carmen Ruiz-Abellón & Luis Alfredo Fernández-Jiménez & Antonio Guillamón & Alberto Falces & Ana García-Garre & Antonio Gabaldón, 2019. "Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation," Energies, MDPI, vol. 13(1), pages 1-31, December.
    50. Amaral, Luiz Felipe & Souza, Reinaldo Castro & Stevenson, Maxwell, 2008. "A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting," International Journal of Forecasting, Elsevier, vol. 24(4), pages 603-615.
    51. Feras Alasali & Husam Foudeh & Esraa Mousa Ali & Khaled Nusair & William Holderbaum, 2021. "Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources," Energies, MDPI, vol. 14(8), pages 1-31, April.
    52. Ciarreta, Aitor & Martinez, Blanca & Nasirov, Shahriyar, 2023. "Forecasting electricity prices using bid data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1253-1271.
    53. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
    54. Mukherjee, Paramita & Coondoo, Dipankor & Lahiri, Poulomi, 2019. "Forecasting Hourly Prices in Indian Spot Electricity Market," MPRA Paper 103161, University Library of Munich, Germany.
    55. Carlo Fezzi & Valeria Fanghella, 2020. "Real-time estimation of the short-run impact of COVID-19 on economic activity using electricity market data," Papers 2007.03477, arXiv.org.
    56. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
    57. Kamal Chapagain & Somsak Kittipiyakul, 2018. "Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables," Energies, MDPI, vol. 11(4), pages 1-34, April.
    58. Rubin, Ofir D. & Babcock, Bruce A., 2011. "A novel approach for modeling deregulated electricity markets," Energy Policy, Elsevier, vol. 39(5), pages 2711-2721, May.
    59. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    60. Jaume Rosselló Nadal & Mohcine Bakhat, 2009. "A new approach to estimating tourism-induced electricity consumption," CRE Working Papers (Documents de treball del CRE) 2009/6, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
    61. Carlo Fezzi & Valeria Fanghella, 2020. "Real-time estimation of the short-run impact of COVID-19 on economic activity using electricity market data," DEM Working Papers 2020/8, Department of Economics and Management.
    62. Rallapalli, Srinivasa Rao & Ghosh, Sajal, 2012. "Forecasting monthly peak demand of electricity in India—A critique," Energy Policy, Elsevier, vol. 45(C), pages 516-520.
    63. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number hsbook0601, December.
    64. Trapero, Juan R. & Pedregal, Diego J., 2009. "Frequency domain methods applied to forecasting electricity markets," Energy Economics, Elsevier, vol. 31(5), pages 727-735, September.
    65. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    66. Lobo, Benjamin J. & Brown, Donald E. & Grazaitis, Peter J., 2019. "Long-term forecasting of fuel demand at theater entry points," International Journal of Forecasting, Elsevier, vol. 35(2), pages 502-520.
    67. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    68. Karamaziotis, Panagiotis I. & Raptis, Achilleas & Nikolopoulos, Konstantinos & Litsiou, Konstantia & Assimakopoulos, Vassilis, 2020. "An empirical investigation of water consumption forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(2), pages 588-606.
    69. Walter, Travis & Price, Phillip N. & Sohn, Michael D., 2014. "Uncertainty estimation improves energy measurement and verification procedures," Applied Energy, Elsevier, vol. 130(C), pages 230-236.
    70. Motlagh, Omid & Paevere, Phillip & Hong, Tang Sai & Grozev, George, 2015. "Analysis of household electricity consumption behaviours: Impact of domestic electricity generation," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 165-178.
    71. Elamin, Niematallah & Fukushige, Mototsugu, 2018. "Modeling and forecasting hourly electricity demand by SARIMAX with interactions," Energy, Elsevier, vol. 165(PB), pages 257-268.
    72. Dutta, Goutam & Mitra, Krishnendranath, 2015. "Dynamic Pricing of Electricity: A Survey of Related Research," IIMA Working Papers WP2015-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    73. Ifiok Anthony Umoren & Muhammad Zeeshan Shakir, 2022. "Electric Vehicle as a Service (EVaaS): Applications, Challenges and Enablers," Energies, MDPI, vol. 15(19), pages 1-23, September.
    74. Cho, Haeran & Goude, Yannig & Brossat, Xavier & Yao, Qiwei, 2013. "Modeling and forecasting daily electricity load curves: a hybrid approach," LSE Research Online Documents on Economics 49634, London School of Economics and Political Science, LSE Library.
    75. Tziolis, Georgios & Spanias, Chrysovalantis & Theodoride, Maria & Theocharides, Spyros & Lopez-Lorente, Javier & Livera, Andreas & Makrides, George & Georghiou, George E., 2023. "Short-term electric net load forecasting for solar-integrated distribution systems based on Bayesian neural networks and statistical post-processing," Energy, Elsevier, vol. 271(C).
    76. Olga Y. Uritskaya & Vadim M. Uritsky, 2015. "Predictability of price movements in deregulated electricity markets," Papers 1505.08117, arXiv.org.
    77. Shakouri, Mahmoud & Lee, Hyun Woo & Kim, Yong-Woo, 2017. "A probabilistic portfolio-based model for financial valuation of community solar," Applied Energy, Elsevier, vol. 191(C), pages 709-726.
    78. Safiullah, Hameed, 2011. "Evaluation of Grid Level Impacts of Electric Vehicles," MPRA Paper 58517, University Library of Munich, Germany.
    79. Motlagh, Omid & Berry, Adam & O'Neil, Lachlan, 2019. "Clustering of residential electricity customers using load time series," Applied Energy, Elsevier, vol. 237(C), pages 11-24.
    80. Carlo Fezzi & Valeria Fanghella, 2020. "Real-Time Estimation of the Short-Run Impact of COVID-19 on Economic Activity Using Electricity Market Data," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 885-900, August.
    81. Zhongwen Li & Chuanzhi Zang & Peng Zeng & Haibin Yu, 2016. "Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty," Energies, MDPI, vol. 9(7), pages 1-16, June.
    82. Chatum Sankalpa & Somsak Kittipiyakul & Seksan Laitrakun, 2022. "Forecasting Short-Term Electricity Load Using Validated Ensemble Learning," Energies, MDPI, vol. 15(22), pages 1-30, November.
    83. Sergey Voronin & Jarmo Partanen, 2013. "Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks," Energies, MDPI, vol. 6(11), pages 1-24, November.
    84. Uritskaya, Olga Y. & Uritsky, Vadim M., 2015. "Predictability of price movements in deregulated electricity markets," Energy Economics, Elsevier, vol. 49(C), pages 72-81.
    85. Bakhat, Mohcine & Rosselló, Jaume, 2011. "Estimation of tourism-induced electricity consumption: The case study of Balearics Islands, Spain," Energy Economics, Elsevier, vol. 33(3), pages 437-444, May.
    86. Seunghyoung Ryu & Jaekoo Noh & Hongseok Kim, 2016. "Deep Neural Network Based Demand Side Short Term Load Forecasting," Energies, MDPI, vol. 10(1), pages 1-20, December.
    87. Saini, Priyesh & Parida, S.K., 2024. "A novel probabilistic gradient boosting model with multi-approach feature selection and iterative seasonal trend decomposition for short-term load forecasting," Energy, Elsevier, vol. 294(C).
    88. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
    89. Oscar Trull & J. Carlos Garc'ia-D'iaz & Angel Peir'o-Signes, 2024. "mshw, a forecasting library to predict short-term electricity demand based on multiple seasonal Holt-Winters," Papers 2402.10982, arXiv.org.
    90. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
    91. María Del Carmen Ruiz-Abellón & Antonio Gabaldón & Antonio Guillamón, 2018. "Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees," Energies, MDPI, vol. 11(8), pages 1-22, August.
    92. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
    93. George P. Papaioannou & Christos Dikaiakos & Anargyros Dramountanis & Panagiotis G. Papaioannou, 2016. "Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoot," Energies, MDPI, vol. 9(8), pages 1-40, August.
    94. Liu, Da & Wang, Jilong & Wang, Hui, 2015. "Short-term wind speed forecasting based on spectral clustering and optimised echo state networks," Renewable Energy, Elsevier, vol. 78(C), pages 599-608.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Patrick Eugene McSharry should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.