Solar Power Generation Forecasting Research Articles

Solar Power Generation Forecasting in Smart Cities and Explanation Based on Explainable AI

Solar Power Generation Forecasting in Smart Cities and Explanation Based on Explainable AI

The Comparison of GRU and LSTM in Solar Power Generation Forecasting Application

Precise forecasting of solar power generation is essential for optimizing the incorporation of renewable energy into the electrical grid and maintaining the stability of energy systems. This paper offers a thorough comparative examination of Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks in forecasting solar power generation. Both models were assessed for predicted accuracy and computational efficiency, employing essential performance indicators including Mean Absolute Error (MAE) and R-squared (R²). The efficacy of LSTM and GRU in forecasting is assessed using a real-world dataset. Comprehensive experimental data demonstrate that the GRU model surpasses the LSTM model.

Predictive Modeling and Forecasting of Solar Power Generation Using Machine Learning Techniques

The purpose of this research study is to investigate the predictive modelling and forecasting of solar power generation by utilising a variety of machine learning approaches. The paper addresses the significant obstacle that is presented by the variable production of solar energy, which is a barrier to the effective incorporation of solar power into the network of electrical power distribution systems. This research investigates the application of machine learning models such as Bayesian Ridge Regression, Gradient Boosting, and Linear Regression. The research makes use of historical meteorological data and solar power output to investigate the application of these models. The effectiveness of these models in estimating the amount of solar energy that will be produced under a variety of different weather situations is being investigated. Based on the findings, it is evident that the application of machine learning techniques has the potential to considerably improve the accuracy of solar power projections. This, in turn, will facilitate better grid integration and promote wider adoption of solar energy products. Through the demonstration of the potential of advanced machine learning approaches in enhancing the reliability and efficiency of solar power generation, this study makes a contribution to the expanding body of literature on the forecasting of renewable energy sources.

Research and application of a novel graph convolutional RVFL and evolutionary equilibrium optimizer algorithm considering spatial factors in ultra-short-term solar power prediction

With the increasing proportion of photovoltaic power generation, solar power prediction systems have become more important. Accurate photovoltaic power prediction can assist the grid dispatch department in formulating effective power scheduling plans, improving grid stability and the capacity to accommodate solar energy. To address the challenges in solar power generation forecasting, the paper proposes a new neural network model called Graph Convolutional Random Vector Functional Link (GCRVFL). The output layer of this model includes both nonlinear transformed features from the hidden layer and the original input features, while also considering the spatial characteristics among photovoltaic sites. Furthermore, Singular Spectrum Analysis (SSA) is utilized to decompose the original solar power time series, extracting different component sequences from the photovoltaic power time series. Opposite based learning and cross-mutation are employed to improve the original Equilibrium Optimizer (EO) algorithm, resulting in an Evolutionary Equilibrium Optimizer (EEO) algorithm. The EEO algorithm is employed to optimize the weight threshold of GCRVFL, leading to a novel photovoltaic power prediction model called SSA-EEO-GCRVFL. Through four datasets and eight control experiments, it can be observed that the proposed model achieves the best predictive performance and is capable of fulfilling the task of photovoltaic power prediction.

Machine Learning Models for Solar Power Generation Forecasting in Microgrid Application Implications for Smart Cities

In the context of escalating concerns about environmental sustainability in smart cities, solar power and other renewable energy sources have emerged as pivotal players in the global effort to curtail greenhouse gas emissions and combat climate change. The precise prediction of solar power generation holds a critical role in the seamless integration and effective management of renewable energy systems within microgrids. This research delves into a comparative analysis of two machine learning models, specifically the Light Gradient Boosting Machine (LGBM) and K Nearest Neighbors (KNN), with the objective of forecasting solar power generation in microgrid applications. The study meticulously evaluates these models’ accuracy, reliability, training times, and memory usage, providing detailed experimental insights into optimizing solar energy utilization and driving environmental sustainability forward. The comparison between the LGBM and KNN models reveals significant performance differences. The LGBM model demonstrates superior accuracy with an R-squared of 0.84 compared to KNN’s 0.77, along with lower Root Mean Squared Error (RMSE: 5.77 vs. 6.93) and Mean Absolute Error (MAE: 3.93 vs. 4.34). However, the LGBM model requires longer training times (120 s vs. 90 s) and higher memory usage (500 MB vs. 300 MB). Despite these computational differences, the LGBM model exhibits stability across diverse time frames and seasons, showing robustness in handling outliers. These findings underscore its suitability for microgrid applications, offering enhanced energy management strategies crucial for advancing environmental sustainability. This research provides essential insights into sustainable practices and lays the foundation for a cleaner energy future, emphasizing the importance of accurate solar power forecasting in microgrid planning and operation.

Hybrid machine learning model combining of CNN-LSTM-RF for time series forecasting of Solar Power Generation

Forecasting solar power generation (SPG) is vital for the development and planning of power systems, offering significant benefits in terms of technical performance and financial efficiency. It enhances system reliability, safety, stability and it reduces the operational costs. This paper's primary goal is to develop models that can precisely forecast solar power generation by analyzing real first-hand dataset of solar power. The value of these forecasting models lies in their ability to anticipate future solar power generation, thus optimizing resource use and minimizing expenses. To achieve this, the study utilizes various classical machine learning, deep learning, and hybrid machine learning techniques, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Recurrent Neural Networks (RNN), Random Forest (RF), Support Vector Regression (SVR), Bi-directional LSTM (Bi-LSTM), and Convolutional Neural Network (CNN). Among these, the hybrid model combining CNN-LSTM-RF demonstrated superior accuracy with R-squared of 92 %, a Root Mean Square Error (RMSE) of 0.07 kW, and a Mean Absolute Error (MAE) of 0.05 kW. This indicates that the hybrid machine learning model combining of CNN-LSTM-RF is effective in forecasting solar power generation.

Long-Term Solar Power Generation Forecasting in the Eastern Coast Region of Malaysia using Artificial Neural Network (ANN) Method

Accurate prediction of power demand and generation is crucial for modern energy systems to efficiently allocate resources and facilitate energy trading. The integration of artificial intelligence (AI) and machine learning techniques has significantly improved the precision of power forecasting. This study focuses on the application of Artificial Neural Networks (ANN) for forecasting power generation in the Eastern Coast region of Malaysia, with a specific emphasis on solar power. The research methodology involves collecting and analyzing historical power data, weather data, and relevant variables. ANN models are trained, validated, and tested on a selected power grid to assess their accuracy and predictive capabilities. The expected outcomes aim to include the development of a precise power generation forecasting model, providing valuable insights for decision-makers to optimize energy operations and seamlessly integrate renewable sources. Additionally, the study explores potential challenges, limitations, and best practices associated with ANN-based power forecasting. The dataset covers the period from 2020 to 2023, with variables such as average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed recorded at 30-minute intervals. The architecture of the ANN model, implemented using the Keras framework, is described as a Sequential model with layers utilizing the 'ReLU' activation function. Model evaluation employs metrics like root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) on the test set, offering insights into the model's overall fit, average deviation, and sensitivity to outliers. Results reveal strong correlations between PV module temperature, irradiance, and AC power generated.

Improving short-term forecasting of solar power generation by using an EEMD-BiGRU model: A comparative study based on seven standalone models and six hybrid models

ABSTRACT Accurate and timely forecasting is critical for grid-connected solar power safety and stability, achieved through machine learning (ML) for both common and real-time applications. To mitigate the impact of nonstationarity and volatility in solar power generation, we employed empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), and complete EEMD with adaptive noise (CEEMDAN) to decompose time series into frequency components, reducing fluctuations and noise. A combination of four decomposition methods (EMD, EEMD, VMD, and CEEMDAN) and two ML models, bidirectional gated recurrent unit (BiGRU) and bidirectional long short-term memory (BiLSTM) were utilized to construct six hybrid forecasting models (EMD-BiLSTM, EMD-BiGRU, EEMD-BiLSTM, EEMD-BiGRU, VMD-BiGRU, and CEEMDAN-BiGRU), which were validated on a dataset from a 20 MW solar power station in Hebei, compared to the seven standalone ML models, backpropagation neural networks (BPNN), support vector machines (SVM), long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), BiLSTM, and BiGRU, these six hybrid models demonstrated enhanced forecast accuracy. Of these, EEMD-BiGRU, VMD-BiGRU, and CEEMDAN-BiGRU significantly reduced prediction errors, with percentage reductions in root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) ranging from 44.18 ~ 49.43%, 43.67 ~ 48.59%, and 44.64% ~53.53%, respectively. The EEMD-BiGRU model outperformed all hybrid models, achieving an RMSE of 0.7662, MAE of 0.3990, MAPE of 7.982%, and R2 of 0.9865. The findings of this study can provide insights and support for applying a hybrid model based on decomposition methods for the short-term forecasting of solar power generation.

Artificial intelligence based succored power prediction in stand alone PV system

ABSTRACT Accurate forecasting of solar power generation is crucial for effective management of renewable energy resources. This literature review emphasises the effectiveness of Artificial Neural Networks (ANNs) in predicting solar energy potential. While traditional ANNs with single hidden layers and predefined neuron configurations have shown success in various applications, they are not suitable for predicting nonlinear time-series databases in the context of this research. The limitations of traditional ANNs in predicting solar power potential necessitate the exploration of alternative approaches. Configuring the structure of traditional ANNs for solar power prediction is a time-consuming process that often requires manual or trial-and-error methods. To address this issue, optimisation techniques are incorporated into the ANN model. Various traditional methods have been explored in this research, but none has achieved satisfactory performance. Drawing from experiences with traditional techniques, a novel Evolution of Cub to Predator (ECP) technique is proposed. The investigation results demonstrate that the incorporation of optimisation techniques yields superior performance compared to traditional ANNs. The proposed ECP technique achieves an impressive prediction accuracy of 97.2%, surpassing existing optimisation techniques. These findings highlight the potential of the ECP technique for accurate solar power prediction and its contribution to optimising renewable energy resource management.

Adaptive solar power generation forecasting using enhanced neural network with weather modulation

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices.

Estimation of Solar Power Generation Through the Use of Effective Forecasting Algorithms

The unpredictable nature of solar power generation poses significant challenges to energy management, particularly in power grids with high solar penetration, leading to potential imbalances between supply and demand. This has increased interest in developing accurate forecasting methods for photovoltaic power generation. This study focuses on short-term forecasting of solar power generation using MATLAB, based on historical generation trends through three different hybrid models. It uses a multilayer perceptron neural network as the forecasting tool, which is optimized through various algorithms to fine-tune the network's hyperparameters, such as the weights and biases of the input and hidden layers, for optimal performance. The Root Mean Square Error is used as the objective metric to minimize the discrepancy between actual and predicted values. The models use Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm, and Genetic Algorithm for network optimization. This research analyzes PV generation data from Belgium in July and August 2022, taken at 15-minute intervals, and evaluates model performance using six different error metrics, including Mean Absolute Error. The results show the accuracy and reliability in forecasting solar power generation time series.

Solar Power Generation Forecasting via Multimodal Feature Fusion (Student Abstract)

Solar power generation has recently been in the spotlight as global warming continues to worsen. However, two significant problems may hinder solar power generation, considering that solar panels are installed outside. The first is soiling, which accumulates on solar panels, and the second is a decrease in sunlight owing to bad weather. In this paper, we will demonstrate that the solar power generation forecasting can increase when considering soiling and sunlight information. We first introduce a dataset containing images of clean and soiled solar panels, sky images, and weather information. For accurate solar power generation forecasting, we propose a new multimodal model that aggregates various features related to weather, soiling, and sunlight. The experimental results demonstrated the high accuracy of our proposed multimodal model.

РЕАЛИЗАЦИЯ ИНТЕЛЛЕКТУАЛЬНОЙ СИСТЕМЫ НЕПРЯМОГО ПРОГНОЗИРОВАНИЯ ВЫРАБАТЫВАЕМОЙ ЭЛЕКТРОЭНЕРГИИ СОЛНЕЧНОЙ ЭЛЕКТРОСТАНЦИИ КАК ПРОГРАММЫ ДЛЯ ЭВМ

The forecasting of electric power generated by a solar power plant enables effective and safe control over electric networks which integrate a cluster of solar power plants. Penalty rates for the purchase of solar power at the day-ahead market, which deviates by more than 5 % of the maximum capacity of solar power plants from the provided hourly model of the day-ahead market of solar power generation, update the accuracy of the day-ahead market model through effective intelligent systems for forecasting solar power generation. It has been found that there is no accessible software for successful forecasting of solar power generation; the advisability and relevance of designing such software with an intelligent system have been shown based on the fi ndings of the examined existing software. The study developed, tested and implemented an intelligent system of indirect forecasting of solar power generation in the form of computer software designed based on a modifi ed fuzzy neural network with an attention mechanism. A class diagram and a block-modular architecture for computer software that implements an intelligent system of indirect forecasting of solar power generation were developed in UML notes using the Microsoft Visio CASE tool. A block-modular architecture provides the fl exibility of computer software. The computer software implementing an intelligent system of indirect forecasting for solar power generation was tested for effectiveness, robust results, and the advisability of its application for building a day-ahead market model. The SCADA database of a solar power plant can be easily integrated with an intelligent system of indirect forecasting of solar power generation.

Recent Advances and Future Challenges of Solar Power Generation Forecasting

Recent Advances and Future Challenges of Solar Power Generation Forecasting

Solar power generation forecasting by a new hybrid cascaded extreme learning method with maximum relevance interaction gain feature selection

Solar power generation forecasting by a new hybrid cascaded extreme learning method with maximum relevance interaction gain feature selection

A VAE-Bayesian deep learning scheme for solar power generation forecasting based on dimensionality reduction

The advancements in distributed generation (DG) technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems. However, the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties. This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory (BiLSTM) neural networks, while handling the high dimensionality in weight parameters using variational auto-encoders (VAE). The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error (RMSE), Pinball loss, etc. Furthermore, reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component. When compared with benchmark methods, the proposed method leads to significant improvements in weight reduction, i.e., from 76,4224 to 2,022 number of weight parameters, quantifying to 97.35% improvement in weight parameters reduction and 37.93% improvement in computational time for 6 months of solar power generation data.

A novel data-driven seasonal multivariable grey model for seasonal time series forecasting

Rapid growth in solar power generation, accompanied by random fluctuations, has important implications for the security, stability, and productivity of the grid. Therefore, accurate predictions of solar power generation provide an essential benchmark for relevant institutions to conduct grid planning and power dispatch. Thereby, a novel data-driven seasonal multivariable grey model is proposed for simulating seasonal fluctuations and non-linear trends in solar power generation. To be specific, the novel model contains the following refinements: first, the seasonal factors and time-power item are introduced to modify the insensitivity of traditional multivariable grey models to seasonal fluctuations and non-linear trends. Second, to enhance the generalizability and adaptability of the model, the parameter associated with the time-power item is determined by using the genetic algorithm. Third, on the premise of proving the model’s requirements for period length and sample size, the derivation of the time response function is expounded. For illustration and verification purposes, comprehensive experimental studies are conducted with quarterly and monthly time series samples. In addition, forecasts of the novel model are compared with five prevailing models, including grey prediction models, traditional econometric technology, and artificial intelligence. Case studies indicate that this novel model exhibits improved generalizability, stability, and reliability in the face of quarterly or monthly time series data, confirming it as an auspicious and powerful tool for future solar power generation forecasting.

Fuzzy Clustered Federated Learning Algorithm for Solar Power Generation Forecasting

Federated learning (FL) is a promising technique to construct a solar power generation forecasting model based on data collected from local generators. However, a set of local generators (i.e., cluster) for FL should be carefully defined to construct a high-accuracy forecasting model. Herein, we propose a fuzzy clustered FL algorithm (FCFLA) where each local generator can be included in more than one cluster. In FCFLA, a local generator has its own membership degree representing its sense of belonging to a specific cluster. Based on this membership degree, FCFLA can generate the high-accuracy forecasting model by catching different characteristics of the data of local generators while addressing the training data shortage problem. Evaluation results demonstrate that FCFLA has the fastest convergence time in achieving the desired accuracy.

Market Value and Agents Benefits of Enhanced Short-Term Solar PV Power Generation Forecasting

Renewable energy sources such as PV solar or wind power are intermittent and non-dispatchable. Massive integration of these resources into the electric mix poses some challenges to meeting power generation with demand. Hence, improving power generation forecasting has raised much interest. This work assesses the market value of enhanced PV solar power generation forecasting. Then, we analyse the different agents present in the electricity system. We link the studied agents to the proposed market values based on both analyses. Improving the accuracy of RES forecasting has massive potential as the sector grows and new agents arise. It can have reactive values like reducing imbalances or proactive values such as participating in intraday markets or exercising energy arbitrage. However, accurate forecasting can also lead to opportunistic values that can be exploited by malicious agents if they are not adequately regulated.

Spatio-Temporal Graph Neural Networks for Multi-Site PV Power Forecasting

Accurate forecasting of solar power generation with fine temporal and spatial resolution is vital for the operation of the power grid. However, state-of-the-art approaches that combine machine learning with numerical weather predictions (NWP) have coarse resolution. In this paper, we take a graph signal processing perspective and model multi-site photovoltaic (PV) production time series as signals on a graph to capture their spatio-temporal dependencies and achieve higher spatial and temporal resolution forecasts. We present two novel graph neural network models for deterministic multi-site PV forecasting dubbed the graph-convolutional long short term memory (GCLSTM) and the graph-convolutional transformer (GCTrafo) models. These methods rely solely on production data and exploit the intuition that PV systems provide a dense network of virtual weather stations. The proposed methods were evaluated in two data sets for an entire year: 1) production data from 304 real PV systems, and 2) simulated production of 1000 PV systems, both distributed over Switzerland. The proposed models outperform state-of-the-art multi-site forecasting methods for prediction horizons of six hours ahead. Furthermore, the proposed models outperform state-of-the-art single-site methods with NWP as inputs on horizons up to four hours ahead.