{"id":{"repo_id":"must-thes","oai_identifier":"oai:scholarsmine.mst.edu:doctoral_dissertations-3478"},"canonical_url":"https://search.dev.ndltd.org/etd/must-thes/oai:scholarsmine.mst.edu:doctoral_dissertations-3478","repository":{"repo_id":"must-thes","name":"Missouri University of Science and Technology","base_url":"https://scholarsmine.mst.edu/do/oai/"},"display":{"title":"Modeling daily electricity load curve using cubic splines and functional principal components","abstract":"\"Forecasting electricity load is very important to the electric utilities as well as producers of power because accurate predictions can cut down costs by avoiding power shortages or surpluses. Of specific interest is the 24-hour daily electricity load profile, which provides insight into periods of high demand and periods where the use of electricity is at a minimum. Researchers have proposed many approaches to modeling electricity prices, real-time load, and day-ahead demand, with varying success. In this dissertation three new approaches to modeling and forecasting the 24-hour daily electricity load profiles are presented. The application of the proposed methods is illustrated using hourly electricity load data from the Atlantic City Electric (AE) zone, which is part of the Pennsylvania, New Jersey, and Maryland (PJM) electricity market. The first approach that is proposed can be used to make short-term forecasts of electricity load. This approach employs a hybrid technique utilizing autoregressive moving average method (ARMA) and cubic spline models. The second approach is suitable for obtaining long-term forecasts of the daily electricity load and employs cubic splines with time varying coefficients. These coefficients are modeled as a multivariate time series using a vector autoregressive model with exogenous variables to forecast the average daily electricity load profile for a future month. The last approach uses functional principal components to model the daily electricity load profile for each day as a linear combination of three eigenfunctions, with the coefficients of the day-specific linear combinations modeled as univariate time series using transfer functions. The fitted models from the three approaches were applied to data from a subsequent year and the results show that these models perform quite well\"--Abstract, page iii.","abstract_html":""Forecasting electricity load is very important to the electric utilities as well as producers of power because accurate predictions can cut down costs by avoiding power shortages or surpluses. Of specific interest is the 24-hour daily electricity load profile, which provides insight into periods of high demand and periods where the use of electricity is at a minimum. Researchers have proposed many approaches to modeling electricity prices, real-time load, and day-ahead demand, with varying success. In this dissertation three new approaches to modeling and forecasting the 24-hour daily electricity load profiles are presented. The application of the proposed methods is illustrated using hourly electricity load data from the Atlantic City Electric (AE) zone, which is part of the Pennsylvania, New Jersey, and Maryland (PJM) electricity market. The first approach that is proposed can be used to make short-term forecasts of electricity load. This approach employs a hybrid technique utilizing autoregressive moving average method (ARMA) and cubic spline models. The second approach is suitable for obtaining long-term forecasts of the daily electricity load and employs cubic splines with time varying coefficients. These coefficients are modeled as a multivariate time series using a vector autoregressive model with exogenous variables to forecast the average daily electricity load profile for a future month. The last approach uses functional principal components to model the daily electricity load profile for each day as a linear combination of three eigenfunctions, with the coefficients of the day-specific linear combinations modeled as univariate time series using transfer functions. The fitted models from the three approaches were applied to data from a subsequent year and the results show that these models perform quite well"--Abstract, page iii.","abstract_has_math":false,"creators":["Jornaz, Abdelmonaem Salem"],"institution":"Missouri University of Science and Technology","degree_name":"Ph. D. in Mathematics","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T03:18:50Z","subjects":["Electricity load","Functional principal components","Splines","VARX","Mathematics","Statistics and Probability"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/2476","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Jornaz, Abdelmonaem Salem"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. 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Of specific interest is the 24-hour daily electricity load profile, which provides insight into periods of high demand and periods where the use of electricity is at a minimum. Researchers have proposed many approaches to modeling electricity prices, real-time load, and day-ahead demand, with varying success. In this dissertation three new approaches to modeling and forecasting the 24-hour daily electricity load profiles are presented. The application of the proposed methods is illustrated using hourly electricity load data from the Atlantic City Electric (AE) zone, which is part of the Pennsylvania, New Jersey, and Maryland (PJM) electricity market. The first approach that is proposed can be used to make short-term forecasts of electricity load. This approach employs a hybrid technique utilizing autoregressive moving average method (ARMA) and cubic spline models. The second approach is suitable for obtaining long-term forecasts of the daily electricity load and employs cubic splines with time varying coefficients. These coefficients are modeled as a multivariate time series using a vector autoregressive model with exogenous variables to forecast the average daily electricity load profile for a future month. The last approach uses functional principal components to model the daily electricity load profile for each day as a linear combination of three eigenfunctions, with the coefficients of the day-specific linear combinations modeled as univariate time series using transfer functions. The fitted models from the three approaches were applied to data from a subsequent year and the results show that these models perform quite well\"--Abstract, page iii."]},{"key":"dc:title","label":"Title","values":["Modeling daily electricity load curve using cubic splines and functional principal components"]}]}],"canonical_facts":{"dc:creator":["Jornaz, Abdelmonaem Salem"],"dc:description.abstract":["\"Forecasting electricity load is very important to the electric utilities as well as producers of power because accurate predictions can cut down costs by avoiding power shortages or surpluses. Of specific interest is the 24-hour daily electricity load profile, which provides insight into periods of high demand and periods where the use of electricity is at a minimum. Researchers have proposed many approaches to modeling electricity prices, real-time load, and day-ahead demand, with varying success. In this dissertation three new approaches to modeling and forecasting the 24-hour daily electricity load profiles are presented. The application of the proposed methods is illustrated using hourly electricity load data from the Atlantic City Electric (AE) zone, which is part of the Pennsylvania, New Jersey, and Maryland (PJM) electricity market. The first approach that is proposed can be used to make short-term forecasts of electricity load. This approach employs a hybrid technique utilizing autoregressive moving average method (ARMA) and cubic spline models. The second approach is suitable for obtaining long-term forecasts of the daily electricity load and employs cubic splines with time varying coefficients. These coefficients are modeled as a multivariate time series using a vector autoregressive model with exogenous variables to forecast the average daily electricity load profile for a future month. The last approach uses functional principal components to model the daily electricity load profile for each day as a linear combination of three eigenfunctions, with the coefficients of the day-specific linear combinations modeled as univariate time series using transfer functions. The fitted models from the three approaches were applied to data from a subsequent year and the results show that these models perform quite well\"--Abstract, page iii."],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/2476"],"dc:subject":["Electricity load","Functional principal components","Splines","VARX","Mathematics","Statistics and Probability"],"dc:title":["Modeling daily electricity load curve using cubic splines and functional principal components"],"dc:type":["Dissertation - Open Access"],"thesis:degree_name":["Ph. D. in Mathematics"],"thesis:institution_name":["Missouri University of Science and Technology"]},"updated_at":"2026-07-24T03:18:50Z"}