General noise support vector regression with non-constant uncertainty intervals for solar radiation prediction

Abstract

General noise cost functions have been recently proposed for support vector regression (SVR). When applied to tasks whose underlying noise distribution is similar to the one assumed for the cost function, these models should perform better than classical -SVR. On the other hand, uncertainty estimates for SVR have received a somewhat limited attention in the literature until now and still have unaddressed problems. Keeping this in mind, three main goals are addressed here. First, we propose a framework that uses a combination of general noise SVR models with naive online R minimization algorithm (NORMA) as optimization method, and then gives nonconstant error intervals dependent upon input data aided by the use of clustering techniques. We give theoretical details required to implement this framework for Laplace, Gaussian, Beta, Weibull and Marshall-Olkin generalized exponential distributions. Second, we test the proposed framework in two real-world regression problems using data of two public competitions about solar energy. Results show the validity of our models and an improvement over classical -SVR. Finally, in accordance with the principle of reproducible research, we make sure that data and model implementations used for the experiments are easily and publicly accessible.

Schedulable capacity forecasting for electric vehicles based on big data analysis

“…In order to evaluate the performance of the proposed PGBDT algorithm for the ultra-short-term and one-dayahead EVSCF models, the mean absolute percentage error (MAPE) and root mean square error (RMSE) are chosen as evaluation indexes. The expressions are shown in (23) and (24), respectively:…”

“…Group one refers to parallel processing of traditional algorithms by using Hadoop and Spark cluster technology [19][20][21]. Group two is the combination of clustering or optimization algorithms and traditional ML algorithms [22][23][24]. Group three is a combination of group one and group two [25][26][27].…”

“…For example, [19] analyses the forecasting time and error for data sets with different sizes in different sizes of Hadoop clusters. In group two, clustering algorithms on large-scale data sets can be used to improve the performance markedly [22,23]. For group three, [25] and [26] propose new hybrid algorithms, which combine the improved particle swarm optimization and extreme learning machine, fuzzy C clustering and support vector machines (SVM), respectively.…”

Inter-hour direct normal irradiance forecast with multiple data types and time-series

“…where C was set in [1,5] at a step size of 0.5, e was set in [0.001, 0.1] at a step size of 0.001, and m was set in [1,3] at a step size of 0.2, respectively. The range of these hyperparameters was set based on the experimental results and previous experience.…”

“…Dramatic fluctuations cause the energy output of a solar power plant to rapidly decrease from hundreds of megawatts to zero output within a few minutes, and bring about huge risk to the stability of the electrical grid [1]. Therefore, an accurate forecast of solar power is the premise and key technology of the grid-connection for photovoltaic (PV) or concentrated solar thermal (CST) plants [2,3]. For various power system operations, the geographical and temporal requirements differ in a solar power forecast [4,5].…”