"Modeling London’s Energy Consumption in Response to Daily Fluctuations in Weather (2011-2014)" investigates the relationship between weather conditions and electricity usage in London. Authored by Emmanuel Epau, Logan Eades, and Thomas Zwiller at the Mendoza College of Business, University of Notre Dame, this study employs multiple forecasting techniques, including Linear Regression, Seasonal ARIMA, Neural Networks, and ensemble models, to predict energy demand. The paper highlights the significance of temperature, precipitation, and sunshine as key predictors. Findings reveal that incorporating weather data enhances forecast accuracy, crucially aiding in managing energy resources and preventing shortages during peak demand periods.
