Short-term power load forecasting is of great significance for the reliable and safe operation systems. In order to improve accuracy short-term forecasting, problems random fluctuation in complexity load-influencing factors, this paper proposes a two-stage method, SSA–VMD-LSTM-MLR-FE (SVLM–FE) based on sparrow search algorithm (SSA), optimize variational mode decomposition (VMD) feature enginee...

This paper proposes an optimal ensemble method for one-day-ahead hourly wind power forecasting. The forecasting is the most common of meteorological Several different models are combined to increase accuracy. proposed has three stages. first stage uses k-means classify generation data into five distinct categories. In second stage, single prediction models, including a K-nearest neighbors (KNN)...

General. Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. From the algorithmic point of view, SSA can be considered as a typical subspace-based method of signal processing. SSA aims at decomposing the original se...

Although over a thousand scientific papers address the topic of load forecasting every year, only a few are dedicated to finding a general framework for load forecasting that improves the performance, without depending on the unique characteristics of a certain task such as geographical location. Meta-learning, a powerful approach for algorithm selection has so far been demonstrated only on uni...

Implementation of multivariate and 2D extensions of singular spectrum analysis (SSA) by means of the R package Rssa is considered. The extensions include MSSA for simultaneous analysis and forecasting of several time series and 2D-SSA for analysis of digital images. A new extension of 2D-SSA analysis called shaped 2D-SSA is introduced for analysis of images of arbitrary shape, not necessary rec...

Monthly runoff forecasting plays a vital role in reservoir ecological operation, which can reduce the negative impact of dam construction and operation on river ecosystem. Numerous studies have been conducted to improve monthly forecast accuracy, machine learning methods paid much attention due their unique advantages. In this study, conjunction model, EEMD-SSA-LSTM for short, comprises ensembl...

<p>The neural network runs slowly and lacks accuracy in power load forecasting, so it is optimized using meta-heuristic algorithm. Salp Swarm Algorithm (SSA) a novel algorithm that simulates the salp foraging process. In this paper, parallel swarm (PSSA) proposed to improve performance of SSA. It not only improves local development capabilities, but also accelerates global exploration. Th...

This paper presents a feedforward neural network approach to sunspot forecasting. The sunspot series were analyzed with feedforward neural networks, formalized based on statistical models. The statistical models were used as comparison models along with recurrent neural networks. The feedforward networks had 24 inputs (depending on the number of predictor variables), one hidden layer with 20 ...

In recent years Singular Spectrum Analysis (SSA), a relatively novel but powerful technique in time series analysis, has been developed and applied to many practical problems across different fields. In this paper we review recent developments in the theoretical and methodological aspects of the SSA from the perspective of analyzing and forecasting economic and financial time series, and also r...