Forecasting Methods for Renewable Power Generation (eBook)

Jai Govind Singh, Rupendra Kumar Pachauri, Sasidharan Sreedharan (Herausgeber)

eBook Download: EPUB

2025
581 Seiten
Wiley-Scrivener (Verlag)
978-1-394-24944-2 (ISBN)

Infos

Forecasting Methods for Renewable Power Generation is an essential resource for both professionals and students, providing in-depth insights into vital forecasting techniques that enhance grid stability, optimize resource management, and enable effective electricity pricing strategies. It is a must-have reference for anyone involved in the clean energy sector.

Forecasting techniques in renewable power generation, demand response, and electricity pricing are vital for grid stability, optimal resource allocation, efficient energy management, and cost-effective electricity supply. They enable grid operators and market participants to make informed decisions, mitigate risks, and enhance the overall reliability and sustainability of the electrical grid. Electricity prices can vary significantly based on supply and demand dynamics. By forecasting expected demand and the availability of generation resources, market operators can optimize electricity pricing strategies. This alignment of prices with anticipated supply-demand balance incentivizes the efficient use of electricity and promotes market efficiency. Accurate forecasting helps prevent price spikes, reduces market uncertainties, and supports the development of effective energy trading strategies.

This book presents these topics and trends in an encyclopedic format, serving as a go-to reference for engineers, scientists, or students interested in the subject. The book is divided into three easy-to-navigate sections that thoroughly examine the AI and machine learning-based algorithms and pseudocode considered in this study. This is the most comprehensive and up-to-date encyclopedia of forecasting in renewable power generation, demand response, and electricity pricing ever written, and is a must-have for any library.

Jai Govind Singh, PhD, is an associate professor in the Department of Energy, Environment, and Climate Change at the School of Environment, Resources, and Development, Asian Institute of Technology, Bangkok, Thailand. He has completed 19 sponsored research projects with various international organizations and has published over 110 research papers in reputed journals and conferences. His wide net of research areas includes e-vehicle technologies, smart grid and micro-grid design and operation, power system operation and control, electricity market restructuring and power trading, and energy storage technologies.

Rupendra Kumar Pachauri, PhD, is an assistant professor in the Electrical and Electronics Engineering Department at the University of Petroleum and Energy Studies, Dehradun, India. He has published over 130 research papers in internationally reputed journals and conferences, as well as several patents. His primary areas of research include solar energy, fuel cell technology, and smart grid operations.

Sasidharan Sreedharan, PhD, is an assistant professor at the College of Applied Sciences, Ministry of Higher Education, Sultanate of Oman. He has completed more than 15 sponsored research projects for various international organizations and published over 80 research papers in reputed journals and conferences. His primary areas of research include high-performance computing, AI and machine learning, optimization and cybersecurity, smart grid operations, electrical supply restructuring, and energy storage.

Textauszug

1
Solar Power Forecasting Using Hybrid Deep Learning Networks Combined with Variational Mode Decomposition

Krishna Prakash Natarajan and Jai Govind Singh*

Department of Energy, Environment and Climate Change, School of Environment Resources and Development, Asian Institute of Technology, Pathum Thani, Thailand

Abstract

Solar power forecasting is beneficial for better operation of electrical systems to satisfy the rising energy demand using renewable energy. However, the uncertainty of solar irradiance degrades the prediction accuracy and model generalization ability. Therefore, hybrid deep learning methods with decomposition techniques are proposed here to ease the impact caused by the nonlinearity and nonstationarity of solar radiation. Firstly, the historical time-series solar power data is decomposed into band-limited intrinsic mode functions using variational mode decomposition. Secondly, the band-limited signals and the multivariate meteorological and time features are independently predicted using the hybrid network; finally, the aggregation results in the solar power prediction. The outcomes are compared with other benchmarked models.

Keywords: Solar power forecasting, hybrid deep learning networks, variational mode decomposition, recurrent neural networks (RNN), long short-term memory (LSTM) networks

1.1 Introduction

Solar power forecasting (SPF) is a rapidly evolving field driven by the growing importance of solar energy in the transition to sustainable and renewable power sources. It helps grid operators manage the integration of variable solar generation into the grid while ensuring grid stability and reliability. Precise solar power forecasts allow operators to proactively respond to output variations and balance supply and demand in real time, minimizing grid disruptions and ensuring smooth operation [1]. Furthermore, forecasting solar power generation is crucial for facilitating renewable energy integration, energy trading, providing stakeholders with information to facilitate informed decision-making, improving system reliability, and unlocking the full potential of solar energy as a clean and abundant resource [2, 3].

There are several types of SPF methods, each with its characteristics, advantages, and limitations. Based on the time horizon, forecasting methods are divided into three types: long-term, medium-term, and short-term power forecasting [4]. Short-term forecasting predicts solar power output over a horizon ranging from a few minutes to several hours ahead. It is essential for operational planning and real-time control of solar power plants, allowing grid operators to optimize energy scheduling and manage grid stability. Medium-term forecasting predicts solar power output over a horizon of several hours to several days ahead. It helps renewable energy developers, grid operators, and market participants make informed decisions about resource allocation, energy trading, and investment planning. Long-term forecasting predicts solar power output over days to weeks or even months ahead. It supports strategic decision-making, such as capacity planning, infrastructure development, and policy formulation, by providing insights into long-term trends and variability in solar power generation [3, 5]. Deterministic forecasting aims to estimate future solar power output based on historical time-series data and meteorological forecasts [6].

The forecasting models are divided into physical, statistical, and deep learning [7]. Physical models of solar irradiance forecasting leverage knowledge of atmospheric physics, radiative transfer processes, and solar geometry to simulate solar radiation under different weather conditions. They account for atmospheric scattering, cloud cover, and terrain effects, offering valuable insights into solar irradiance variability. However, physical models require detailed input parameters and may be computationally intensive [8].

Early research in SPF predominantly relied on statistical methods, such as time-series analysis, autoregressive integrated moving averages, and exponential smoothing techniques. While effective for short-term forecasting, statistical methods may struggle to capture nonlinear and complex relationships in solar irradiance data [9, 10]. Machine learning (ML) techniques, particularly artificial neural networks (ANNs), have gained popularity in solar power and irradiance forecasting owing to their ability to model complex, nonlinear relationships [11]. ANNs, including feedforward and recurrent neural networks (RNNs), have been extensively used to predict solar power based on meteorological data, such as temperature, humidity, and cloud cover. Random forests, K-nearest neighbor, support vector machines, and gradient boosting algorithms are also employed for solar power or irradiance forecasting [12–15]. ML approaches often outperform traditional statistical methods, especially in capturing nonlinear dependencies and improving forecast accuracy [16].

Hybrid forecasting approaches aim to overcome the limitations of individual methods and improve forecast accuracy across different time horizons. In recent years, with the emergence of deep learning, RNN [17], long short-term memory networks (LSTMs) [18], and convolutional neural networks [19–21], have become the essential units of hybrid models in renewable energy forecasting. Furthermore, integrated hybrid models may leverage the distinct characteristics of many models to predict future data patterns based on historical data. However, relying solely on hybrid models will not yield all the useful information in the dataset. As a result, integrated hybrid models frequently combine with data decomposition to strengthen their anti-interference capacity. Signal decomposition, therefore, becomes a critical component of data processing. Wavelet decomposition [22], empirical mode decomposition [23], and variational mode decomposition (VMD) [24] are commonly used decomposition methods. Combining these methods with deep learning models achieves better accuracy and performance.

1.2 Methodology

This section presents the models and theories used in the proposed forecasting framework.

1.2.1 Variational Mode Decomposition

VMD [25] decomposes a signal into a specified number of band-limited intrinsic mode functions (IMFs) or modes that represent the underlying oscillatory components of the signal. VMD decomposes a real-valued input signal f into discrete sub-signals or modes, uk, with specific sparsity properties. Each mode k is considered compact around a central pulsation ωk, which will be determined alongside the decomposition. The goal is to determine a specific set of IMFs uk and associated center frequencies ωk that yield the minimum value for the constrained variational problem, defined by

(1.1)

where {uk}={u1,…, uk} and {ωk}={ω1,…, ωk} are the set of all modes and their center frequencies, respectively, δ is Dirac function, ||. ||2 represents the L2 distance, and * represents the convolution operation.

The constraint variation problem is transformed into a nonconstraint variation by using the second penalty component α and the Lagrangian multiplication operator λ(t).

(1.2)

The “saddle point” of the expanded Lagrange formula is found by updating ukn+1, ωkn+1, and λkn+1 using the alternate direction method of multipliers (ADMM). In order to update the mode uk(t), the following sub-optimization problem is considered at the n-th iteration.

(1.3)

This is resolved in the spectral domain in VMD, leading to an update in the frequency domain.

(1.4)

In the next step, the center frequency ωk is updated by solving the following sub-optimization problem iteratively

(1.5)

to get an update for ωkn+1 in the dual frequency domain.

(1.6)

The new frequency is estimated as the centroid of the power spectrum of the associated modes. Overall, the VMD updates all of the sub-signals constantly via the frequency domain and subsequently converts them into the time domain using the inverse Fourier transform.

1.2.2 Long Short-Term Memory

The RNN effectively models time-series data. Unlike traditional feedforward neural networks, the output is feedback to the input, acting as dynamic memory to process the input sequence. RNN comprises an input layer, an output layer, a recurrent layer, a series of weight matrices, and activation functions. The weights are shared between hidden units across each time step, and the network produces the same outcome by performing the same task on all layers as shown in Figure 1.1. For each time step t, the activation function and the predicted output ŷ are expressed as follows:

(1.7)

(1.8)

(1.9)

Figure 1.1 Traditional RNN.

where X is an input vector, ht is a hidden state vector at time t, g is the logistic function, b are bias vectors, and Wx, Wh, and Wy are...

Produkt-Details

• Erscheint lt. Verlag: 18.3.2025
• Sprache: englisch
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• ISBN-10: 1-394-24944-6 / 1394249446
• ISBN-13: 978-1-394-24944-2 / 9781394249442