Abstract
Long-term electricity load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment on the construction of excess power facilities, while an underestimate of the future load will result in insufficient generation and inadequate demand. As a first of its kind, this research proposes the use of a multiplicative error model (MEM) in forecasting electricity load for the long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, as accessed from a United States (U.S.) regional transmission operator, and recession data, accessed from the National Bureau of Economic Research, are used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. Historical volatility is used to account for implied volatility. To incorporate future volatility, backtesting of MEM is performed. Two performance indicators used to assess the proposed model are: (i) loss functions in terms of mean absolute percentage error and mean squared error (for both in-sample model fit and out-of-sample forecasts) and (ii) directional accuracy.
Highlights
Load forecasting in the long-term horizon is important for electric utilities and planners in terms of grid expansion planning, future investments, and revenue analysis for long-term decision-making process
As we look into the future, in role as we move towards the declined use of de-carbonized heat pumps in the residential sector and the energy consumption scenario, it is expected that electricity will play a major role as we move the addition electricuse vehicles (EVs) and other in the transportation
Result analysis consists of two parts: first part consist of in-sample model fit using load and Result analysis consists of two parts: first part consist of in-sample model fit using load and economic data followed by out-of-sample forecast, and the second part is checking directional accuracy economic data followed by out-of-sample forecast, and the second part is checking directional by forecasting for the year 2008 during the great economic recession
Summary
Load forecasting in the long-term horizon is important for electric utilities and planners in terms of grid expansion planning, future investments, and revenue analysis for long-term decision-making process. It plays a crucial role in the economic and social development of a country (or specific region in case of some utilities). Annual load forecasting is favored among utilities and is one of the common long-term load forecasting problems. A load forecasting model aims at a mathematical representation of the relationship between load and influential parameters Such a model is identified with coefficients that are used to forecast future values by extrapolating the relationship to the desired lead time.
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