Electricity Consumption (Kw) Forecast for a Building of Interest Based on a Time Series Nonlinear Regression Model

Asian Journal of Economics, Business and Accounting, Volume 23, Issue 21, page 197-207, 2023

11 Pages Posted: 19 Dec 2023

See all articles by Olajide O. Omogoroye

Olajide O. Omogoroye

Independent

Oluwaseun Oladeji Olaniyi

University of the Cumberlands

Olubukola Omolara Adebiyi

Ulster University - Centre City House

Tunboson Oyewale Oladoyinbo

University of Maryland University College (UMUC)

Folashade Gloria Olaniyi

Independent

Date Written: October 20, 2023

Abstract

This paper investigates the relationship between a building's past energy consumption and the outdoor temperature and predicts the next day's energy consumption using a refined time series model. Maintaining optimal indoor temperatures relative to outdoor temperatures determines a building's HVAC demand and, thus, energy consumption. We want to determine how outdoor temperature and other factors determine this consumption. With increasing urbanization and energy demand, it is important to understand building energy consumption, especially in terms of its impact on the environment. Previous research has shown the link between electricity consumption and external environmental factors and highlighted energy optimization's importance in urban structures. As cities become large energy consumers, studies point to the need to understand energy use patterns on a regional and temporal scale. For accurate energy forecasts, data becomes the linchpin. Time series—data points arranged in chronological intervals—are foundational in predictive modeling. Due to buildings' intricate electricity consumption patterns, traditional linear forecasting often falls short. Enter nonlinear regression models: These complex models are apt for mapping and predicting nonlinear data trends. Notwithstanding their advantages, they come with challenges, primarily the high-frequency data influx from smart meters and IoT devices. But their potential benefits - from cost savings to efficient energy management - are significant. In a world caught between urban expansion and ecological preservation, efficient energy management is crucial. Accurate energy forecasting, especially for buildings, combines technological advances, statistical acumen and environmental imperatives. Understanding building energy consumption using sophisticated nonlinear regression models is evolving from an academic goal to a global necessity.

Keywords: Building; energy; consumption; temperature; forecast; time series model; heating; ventilation; electricity; environmental implications; CO2 emissions

Suggested Citation

Omogoroye, Olajide Oyebola and Olaniyi, Oluwaseun Oladeji and Adebiyi, Olubukola Omolara and Oladoyinbo, Tunboson Oyewale and Olaniyi, Folashade Gloria, Electricity Consumption (Kw) Forecast for a Building of Interest Based on a Time Series Nonlinear Regression Model (October 20, 2023). Asian Journal of Economics, Business and Accounting, Volume 23, Issue 21, page 197-207, 2023, Available at SSRN: https://ssrn.com/abstract=4610332

Oluwaseun Oladeji Olaniyi

University of the Cumberlands ( email )

6178 College Station Drive
Williamsburg, KY 40769
United States

HOME PAGE: http://www.ucumberlands.edu

Olubukola Omolara Adebiyi

Ulster University - Centre City House

Tunboson Oyewale Oladoyinbo

University of Maryland University College (UMUC)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
115
Abstract Views
377
Rank
460,851
PlumX Metrics