Abstract
Annual electricity consumption forecasting is one of the important foundations of power system planning. Considering that the long-term electricity consumption curves of developing countries usually present approximately exponential growth trends and linear and accelerated growth rate trends may also appear in certain periods, this paper first proposes a small-sample adaptive hybrid model (AHM) to extrapolate the above curves. The iterative trend extrapolation equation of the proposed model can simulate the linear, exponential, and steep trends adaptively at the same time. To estimate the equation parameters using small samples, the partial least squares (PLS) and iteration starting point optimization algorithms are suggested. To evaluate forecasting performance, the artificial neural network (ANN), grey model (GM), and AHM are used to forecast electricity consumption in China from 1991 to 2014, and then the results of these models are compared. Analysis of the forecasting results shows that the AHM can overcome stochastic changes and respond quickly to changes in the main electricity consumption trend because of its specialized equation structure. Overall error analysis indicators also show that AHM often obtains more precise forecasting results than the other two models.
Highlights
Given that electricity cannot be stored at a large scale, forecasting electricity consumption is important for power enterprises to balance supply and demand
This study mainly focuses on the annual electricity consumption forecasting for developing countries
Among the artificial neural network (ANN), the back-propagation (BP) and radial basis function (RBF) are the most represented models. While the latter is usually better than the former in terms of learning efficiency and stability [10], the RBF ANN was selected for this study
Summary
Given that electricity cannot be stored at a large scale, forecasting electricity consumption is important for power enterprises to balance supply and demand. The long-term annual electricity consumption curves for most developing countries usually present increasing trends with different curvatures [2] That is, these annual electricity consumption curves are often approximately exponential growth curves. The artificial neural network (ANN) and other similar models have been extensively used in trend extrapolation of short-term or monthly electricity consumption time series [9,10,11] Compared with these time series, annual electricity consumption has fewer data and, more importantly, is affected by socioeconomic environment changes. The forecasting equation of GM(1, 1) is essentially a homogeneous exponential equation, which can only perfectly simulate the time series with a steady growth rate This makes it unable to perfectly adapt the characteristics of the real annual electricity consumption data. By adjusting the equation parameters using small samples, this equation can simulate the above three trends adaptively at the same time
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
