Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors

Abstract

Effective energy planning and governmental decision making policies heavily rely on accurate forecast of energy demand. This paper discusses and compares five different forecasting techniques to model energy demand in the United States using economic and demographic factors. Two Artificial Neural Network (ANN) models, two regression analysis models and one autoregressive integrated moving average (ARIMA) model are developed based on historical data from 1950–2013. While ANN model 1 and regression model 1 use Gross Domestic Product (GDP), Gross National Product (GNP) and per capita personal income as independent input factors, ANN model 2 and regression model 2 employ GDP, GNP and population (POP) as the predictive factors. The forecasted values resulted from these models are compared with the forecast made by the U.S. Energy Information Administration (EIA) for the period of 2014–2019. The forecasted results of ANN models and regression model 1 are close to those of the U.S. EIA, however the results of regression model 2 and ARIMA model are significantly different from the forecast made by the U.S. EIA. Finally, a comparison of the forecasted values resulted from three efficient models showed the energy demand would vary between 95.51 and 100.08 quadrillion British thermal unit for the period of 2014–2019.

Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network

“…The results showed that the error of the net consumption of energy consumption obtained via artificial neural network method was very small [15]. Deka (2016) compared five different forecasting technologies using economic and demographic factors to simulate US energy needs with in-depth discussion [16]. Torrini (2016) proposed a fuzzy logic approach to extract rules from input variables and to provide Brazil's long-term annual electricity demand forecast [17].…”

“…According to the World Population Prospects (2015) [54], the Chinese population will reach 1.424 billion by 2030. Using Equation (16) to calculate the annual growth rate of the population, it can be inferred that the Chinese approximate growth rate of the population from 2016 to 2021 will be 0.25%. Since there is no authoritative estimate of import and export trade in the world, the initial growth rate, average growth rate and minimum growth rate can only be calculated based on historical data of growth.…”

“…(16), and Chinese primary energy consumption witnesses the fastest decline in this scenario. To sum up, the four scenarios predict that Chinese energy consumption in 2021 will fluctuate between 2954.04 Mtoe and 5618.67 Mtoe.…”

Domestic sector energy demand and prediction models for Punjab Pakistan

“…For reduce in carbon emissions from the domestic sector, it is necessary to understand the interaction of the factors which are responsible for each type of energy demand [10]. The theory behind the data to be collected to achieve this understanding is based on well-established building energy modelling principles [11] [12] which show that a building's services energy demand is mainly determined by fabric, area, location, and control. In contrast, occupant energy demand is considered [13] to be driven mainly by the number of occupants, activities undertaken in the building and economic strength.…”

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

“…(1) Time series data is differentiated in order to make it stationary first which is achieved by making both variances and mean constant [10]; (2) Determining p and q orders by studying the autocorrelation and partial autocorrelation coefficients [13,14]; (3) Validating the selected models by applying diagnostic tests that provide information about the residuals being white noise [2,8]. Detail of each of these steps is given under:…”