Artificial intelligence-driven power demand estimation and short-, medium-, and long-term forecasting

Applications of Artificial Intelligence for Demand Forecasting

“…For instance, (Roach et al, 2021) found that mixed models may increase forecasting accuracy and measure variations in energy usage in numerous designing configuration cases. (Dwivedi & Gupta, 2022) highlighted that the hybrid modeling approach, which leverages long-term autoregression or moving average trend along with economic growth, has provided credible macro-level and long-term forecasting results. Recently, (Dieudonné et al, 2023) proposed a hybrid model based on ANN models, multiple linear regression, and Holt exponential smoothing for short-term electricity.…”

“…For example, supply chain participants make numerous correct operational, tactical, and strategic decisions in various areas, such as production planning, sales budgeting, new product launches, and promotion planning (Abolghasemi et al, 2020;Danese & Kalchschmidt, 2011;Mobarakeh et al, 2017;Perera et al, 2019), purchase quantity (Brahmadeep & Thomassey, 2016), inventory management (Mobarakeh et al, 2017). Additionally, governments and policymakers can offer appropriate policies to support decisions about imports, tariff design, maintenance, system expansion, and development of new projects, particularly in national industries, such as energy (Brentan et al, 2017;Dwivedi & Gupta, 2022;Panapakidis & Dagoumas, 2017;Raza & Khosravi, 2015), agriculture (Rafael González Perea et al, 2019), public transport (Liyanage et al, 2022), healthcare (Soltani et al, 2022). As a result, accurate forecasting is essential because of its implications and roles.…”