Implications of Machine Learning in Renewable Energy ^†

Abstract

Artificial neural networks (ANN) are preferred over some other machine learning (ML) techniques due to their extension potential. The requirement for using ML approaches in the renewable energy market is expected to rise significantly in the upcoming decades due to the huge market for graduate institutions in research, mathematics, and technology connected to machine learning. The collection of data, administration, and protection are predicted to play critical roles in the effective deployment of ML techniques that may be distributed among the main players in the renewable energy industry, hence fostering the creation of large smart energy schemes. The integration of new techniques for generating accurate data, as well as other pieces of knowledge, will improve the communication of data among ML and networks. Both supervised and unsupervised learning are likely to play important roles in the renewable energy industry; however, this will hinge on the development of certain other significant topics in machine learning, like big data analytics (BDA). Because the renewable energy business is dependent on weather, forecasting is an essential aspect of renewables. Machine learning algorithms aid in the precise prediction of renewables.

1. Introduction

A worldwide consensus exists, believing that expanded use by the promotion of sources of renewable energy is vital for the mitigation of weather variation strategies [1]. This article focuses on wind and solar energy. Renewable energies, like ocean energy and bio-power, have also been considered, and have undergone exponential improvement in several places. The source’s intermittent nature is the key obstacle that now hinders renewables from playing a larger part in the electricity sector. Because of this source insecurity, it is critical to build reliable forecast models to forecast electricity created by renewables.

Forecasting is essential in the administration of renewables, particularly when dealing with significant aspects similar to solar and wind power. Estimating research in the field of renewable energy is extensive, with numerous scenarios and structures presented and examined regarding their efficacy for technological solutions. Wind energy production is the industry with the most suggested estimation techniques [2].

The structure of this paper is planned as follows: Section 2 discusses feature extraction in machine learning; Section 3 discusses the application of machine learning for developing renewable technologies; machine learning applications in wind energy are discussed in Section 4; machine learning applications in solar energy are discussed in Section 5; Section 6 discusses application-specific machine learning; and lastly, Section 7 concludes the study with some closing observations.

1.1. Machine Learning

Machine-learning methods have been extensively employed in many domains related to information issues over the past few years. Several interdisciplinary [3] fields are covered by machine-learning algorithms: statistical, math, and neural networks, as well as artificial intelligence, are among the examples. Machine-learning tactics attempt to determine networks among I/P and O/P data by either using or not using math symbols for certain issues. After this step, the training set includes the machine-learning algorithms, and policymakers can acquire pleasing and correct prediction output by inputting the forecasted data into the trained concepts. The field of machine learning primarily uses three learning procedures: supervised learning, unsupervised learning, and reinforcement learning [4].

1.2. Renewables

With the quick advancement of worldwide development, it has become clear that the overuse of fossil energies is not only hastening the reduction in fossil energies, but also have a negative influence on the atmosphere. These factors will lead to greater health hazards and worldwide environmental challenges. Renewable energy is energy that may be reclaimed from nature and reused, including solar power, wind energy, hydroelectric energy, biomass power, and geothermal power. Among the most pressing issues facing renewables in the coming years is their power source. As methods of producing electric power from renewable bases have become more prevalent, it grows increasingly critical to create appropriate technology for storing renewables [5]. Several studies have indicated that different machine-learning procedures have been utilized to forecast renewables. Data-driven algorithms should provide useful methods for predicting the activity of renewables [6].

2. Feature Selection in Machine Learning

Choosing the right function is a significant challenge in machine learning, since feature collection methods engaged in the training process of diverse forecasting algorithms can raise the arrangement’s price and functioning period, as well as its forecast accuracy—in a broader sense, this is feature selection for a learned issue using data [7]. Several studies have merged wrapper and filter strategies to create hybrid methods. These have demonstrated exceptional efficiency across an array of areas [8]. The research that follows includes some feature selections in the areas of wind and solar power.

Increasingly sophisticated computer techniques have lately been used to choose features that pose difficulties for wind power prediction. In [9], for a challenge of probabilistic prediction of wind energy, a continuous method for choosing features using a wrapped GP for prediction was developed. Tests with actual data from multiple windmills in the Baotou area of China demonstrated the efficiency of the suggested technique, which enabled the estimation of short-term airspeed. This method retained the largest set of characteristics discovered during the scan.

In [10], in the interest of reconstructing a dataset of types of climates, we used a technique for feature extraction in order to choose the optimal collection of input variables for an SVM algorithm. Because these types of climates are relevant to the efficiency of solar energy production, much more relevant attributes were selected from solar data and used as inputs for an SVM to recreate the climate variety dataset. This is a cataloging model, which serves as a preliminary stage in predicting PV energy production for houses.

3. Application of Machine Learning to Developing Renewable Technologies

Wind farm plants, often called cyclical turbines, generate power by transforming the motion of moving air into electrical power through the use of a generator. Windmills are categorized into two groups: horizontal-axis wind turbines (HAWT) and vertical-axis wind turbines (VAWT). HAWTs are the best and most frequently employed, as they may be built at greater sizes than VAWTs and, hence, possess greater power generation capabilities.

Productivity arcs are most often used to measure the wind speed and power production of windmills. Learning algorithms, on the other hand, may be employed to serve the same purpose. The estimated energy output is based solely on wind speed data received from five various sites. Within that work, various ML techniques, such as LASSO regression, K closest neighbor, random forest, and support vector regression, were employed to estimate the energy O/P over lengthy time frames (up to 12 months). The study revealed that if sufficient historical information were accessible, the random forest approach could be employed to predict long-term energy production for diverse sites [11].

Solar thermal capture systems and photovoltaic panels are indeed the primary approaches to obtaining electricity from the sun. Solar thermal gatherers are systems that focus solar energy into a laser to warm a mass transfer; on a small level, these are typically used to boil water or places, while on a large scale, they are employed to generate power. Learning algorithms are being utilized to improve existing solar module and solar energy plant construction methods and components. The absence of structured and readily accessible methods is a significant barrier to implementing teaching approaches in this industry. The goal of this research was to discover and define the causes that cause effectiveness to deteriorate. These data were also utilized to develop a concept for even more robust perovskite devices. Out of the obtained data, a decision tree framework was utilized to create inferences and constraints describing the reduction in effectiveness as a result of time [12].

4. Machine Learning Application in Wind Energy

Table 1. Machine-learning implications in wind energy.

Ref. No.	Output/Forecasting	Methods	Advantages	Modeling Elements
[11,13]	Energy output can be predicted in the short and long term.	Support vector machines, neural networks, and regression models.	Models adapt to severe weather situations.	Wind speed, humidity, pressure, etc.
[14]	Wind farms are prone to breakdowns, owing to exposure to weather and rotating parts.	Neural networks, decision trees, and k-nearest neighbor.	Observing windmills with machine learning models lowers on-site operations.	Information from the past

shows some machine-learning implications in wind energy.

5. Machine Learning Applications in Solar Energy

Table 2. Machine-learning implications in solar energy.

Ref. No.	Output/Forecasting	Methods	Advantages	Modeling Elements
[15,16]	Climatic conditions are predicted.	Random forest, deep neural networks, and support vector machines.	Model predictive ability is being improved.	Humidity, pressure, solar radiation, and temperature.
[17]	Predicted solar power outputs	Hybrid methods, artificial neural networks, and support vector machines.	Measuring precision is lacking.	Data on power generation in the past; date and time information.

shows some machine-learning implications in solar energy.

6. Application-Specific Machine Learning

This section describes the current advances in ML approaches that have a direct effect on electrical generation in domains that encourage renewables in industry and society. Several applications of machine learning techniques are discussed in this section. We concentrate on technologies that are projected to have a significant long-term renewable impact in the coming years, namely, forms of renewable energy (wind energy, solar energy, and hydroelectric energy). In [18], wind power is related to ML approaches for the purpose of detecting apparatus failures. The study examines data collection methodologies, types of data, and model training possibilities. For solar power, using ensemble methods, the exchange between complication and forecasting was examined [19], as was the use of machine learning in hydroelectric plants. Also included are ML approaches to short-term hydroelectric management [20].

Statistical techniques were applied in the beginning phases of wind energy forecasting. The current research uses machine learning and AI approaches to anticipate wind energy, as well as classification techniques such as random forest [21]. In [22], SVM (support vector machine) was used; in [23], the LSTM network (large short-term memory); and in [24], ANN (artificial neural network). Wavelet decomposition, wavelet packet decomposition, and ensemble empirical mode decomposing were used to remove noisy effects from the original information and to effectively enhance wind-speed estimates [25]. The hybrids’ wind energy was predicted using a Bayesian algorithm. The analytical findings showed that the Bayesian model-founded technique outperformed the other prediction method in terms of predicting hybrid wind energy [26].

Solar radiation could be modeled as a time series with multiple temporal spans in the solar estimation approaches. Deep-learning methods and approaches, like support-vector machines [27], as well as artificial neural networks (ANN) [28], may be utilized. Information forecast models have already been thriving. A hybrid algorithm of ANFIS with gray wolf optimization has been proposed to anticipate electricity production for the purpose of hydroelectric forecasting [29].

7. Conclusions

The demand forecast for the generation of renewables is becoming critical, and numerous research works have been conducted. Machine-learning methods have become increasingly prominent in the forecasting of renewables. The following are a few potential future study paths for machine-learning algorithms in renewables forecasting. Many machine-learning technologies concerns in estimates of renewables have indeed been centered on solar- or wind-power estimates. As a result, various types of renewable-energy projections, including bioenergy, hydropower, and geothermal energy, might be viable future research areas.

Furthermore, methods involving AI and hybrids may be useful methods for projecting renewables. Forecast presentations of machine-learning algorithms in projections of renewables are influenced by data pre-processing procedures. As a result, examining data preparation approaches and machine-learning algorithms in projections of renewables might be an additional possibility for further studies. Machine-learning parameter collection is another promising field of study to be explored in the upcoming years.