Abstract

As intermittent renewable energy sources such as wind and solar proliferate, the power systems operation and planning become more complicated due to increased uncertainties and variabilities. Accurate forecasting of these sources facilitates planning and operating the electric grid to integrate wind/solar power more reliably and efficiently. The neural network learning process can be disrupted by anomalies of wind/solar time-series data, which results in less accurate forecasting. By processing and analyzing wind/solar time-series data, machine learning and pattern recognition methods such as data clustering and classification can significantly enhance the forecast accuracy. This chapter reviews the various machine learning and pattern recognition methods proposed in the literature for time-series forecasting of solar radiation.

Keywords: forecasting, pattern recognition, renewable energy, solar, time series

#### 1. Introduction

Pattern recognition is the analysis of data to detect their patterns and arrangements [1]. Pattern recognition provides a signal identification technique that is popularly applied in time-series forecasting. Pattern recognition is particularly essential for highly fluctuating and volatile time series such as solar radiation with irregular patterns. The chaotic nature of solar radiation time-series data disrupts the neural network learning process and imposes high errors on forecasting. By better detecting anomalous data points (outliers) and irregular patterns, pattern recognition and machine learning-based techniques characterize the training data more accurately and provide better learning results for neural networks [2]. In this chapter, an overview of the most commonly used pattern recognition and machine learning methods for solar radiation forecasting is presented.

#### 2. Pattern recognition methods in solar forecasting

Several pattern recognition methods have been used to identify patterns and provide a pattern-based prediction technique for solar irradiance [4, 6–8, 10, 13, 14, 16–21, 23–25]. A brief description is provided for each method in the following sections.

## 2.1 Combination of SOM, SVR, and PSO

Self-organizing map (SOM) is an unsupervised learning approach for data classification and clustering [2]. Support vector machines (SVMs) provide supervised learning for data classification and regression analysis [3]. Dong et al. [4] proposes a hybrid forecasting method combining SOM, support vector regression (SVR), and particle swarm optimization (PSO) methods. The SOM is used to divide the input space into disjointed regions with different characteristic information on the correlation between the input and the output. Then, the SVR is applied to model each disjointed region to identify the characteristic correlation. Finally, the PSO is applied to reduce the performance volatility of SVR with different parameters. The hybrid SOM-SVR-PSO method is rigorously tested and compared with several wellknown time series forecasting algorithms. The comparison demonstrates higher accuracy of the proposed forecasting method.

#### 2.2 Fuzzy c-means-based method

Fuzzy c-means (FCM) clustering is a well-known data clustering approach that allows each data element to belong to multiple clusters with varying degrees of membership [5]. Boata and Gravila [6] propose a novel method to forecast the stochastic component of the solar irradiation by the sky condition. By adopting the fuzzy inference system (FIS) principles, the proposed model forecasts daily clearness index. The FIS uses fuzzy logic to map any given input (features in the case of fuzzy clustering) to an output (clusters in the case of fuzzy clustering). In the proposed method, fuzzy c-means clustering is used to establish the membership functions (MFs) from the input variable's attributes. The MFs constitute the building blocks of the fuzzy set theory in fuzzy logic and characterize the fuzziness in a fuzzy set. The proposed method is evaluated and concluded to produce highly accurate results for practical applications.

#### 2.3 Combination of fuzzy logic and neural networks

Fuzzy logic provides a powerful pattern recognition tool mainly because of its capability to characterize imperfect or noisy information and to measure data resemblance for clustering [7]. Chen et al. [8] proposes a new technique for solar radiation forecasting by combining the fuzzy logic and neural networks, to achieve a good accuracy at different conditions. In this method, the future sky conditions and temperature information are clustered to different fuzzy sets using a fuzzy logic-based clustering algorithm. The results demonstrate that the hybrid of fuzzy logic and neural network enhances the forecast accuracy for different sky and temperature conditions.

#### 2.4 Spectral clustering

A new time-series clustering technique is proposed in Ref. [9] to reduce the computational complexity of smart grid optimization problems. Spectral clustering is used in this chapter to cluster different profiles for N days, where each day's profile is a time series over T slots. The data are clustered with respect to three different features including time attribute, frequency attribute, and weighted average of time-frequency attributes. The results show that clustering time-series data to provide two or more sub time series can improve the optimization performance when compared to using just a single typical time series.
