Solar Power Generation Forecasting Research Articles

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

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

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.

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.

Charge Scheduling Optimization of Plug-In Electric Vehicle in a PV Powered Grid-Connected Charging Station Based on Day-Ahead Solar Energy Forecasting in Australia

Optimal charge scheduling of electric vehicles in solar-powered charging stations based on day-ahead forecasting of solar power generation is proposed in this paper. The proposed algorithm’s major objective is to schedule EV charging based on the availability of solar PV power to minimize the total charging costs. The efficacy of the proposed algorithm is validated for a small-scale system with a capacity of 3.45 kW and a single charging point, and the annual cost analysis is carried out by modelling a 65 kWp solar-powered EV charging station The reliability and cost saving of the proposed optimal scheduling algorithm along with the integration and the solar PV system is validated for a charging station with a 65 kW solar PV system having charging points with different charging powers. A comprehensive comparison of uncontrolled charging, optimal charging without solar PV system, and optimal charging with solar PV system for different vehicles and different time slots are presented and discussed. From the results, it can be realized that the proposed charging algorithm reduces the overall charging cost from 10–20% without a PV system, and while integrating a solar PV system with the proposed charging method, a cost saving of 50–100% can be achieved. Based on the selected location, system size, and charging points, it is realized that the annual charging cost under an uncontrolled approach is AUS $28,131. On the other hand, vehicle charging becomes completely sustainable with net-zero energy consumption from the grid and net annual revenue of AUS $28,134.445 can be generated by the operator. New South Wales (NSW), Australia is selected as the location for the study. For the analysis Time-Of-Use pricing (ToUP) scheme and solar feed-in tariff of New South Wales (NSW), Australia is adopted, and the daily power generation of the PV system is computed using the real-time data on an hourly basis for the selected location. The power forecasting is carried out using an ANN-based forecast model and is developed using MATLAB and trained using the Levenberg–Marquardt algorithm. Overall, a prediction accuracy of 99.61% was achieved using the selected algorithm.

Sensitive Parameter Analysis for Solar Irradiance Short-Term Forecasting: Application to LoRa-Based Monitoring Technology.

Due to the relevant penetration of solar PV power plants, an accurate power generation forecasting of these installations is crucial to provide both reliability and stability of current grids. At the same time, PV monitoring requirements are more and more demanded by different agents to provide reliable information regarding performances, efficiencies, and possible predictive maintenance tasks. Under this framework, this paper proposes a methodology to evaluate different LoRa-based PV monitoring architectures and node layouts in terms of short-term solar power generation forecasting. A random forest model is proposed as forecasting method, simplifying the forecasting problem especially when the time series exhibits heteroscedasticity, nonstationarity, and multiple seasonal cycles. This approach provides a sensitive analysis of LoRa parameters in terms of node layout, loss of data, spreading factor and short time intervals to evaluate their influence on PV forecasting accuracy. A case example located in the southeast of Spain is included in the paper to evaluate the proposed analysis. This methodology is applicable to other locations, as well as different LoRa configurations, parameters, and networks structures; providing detailed analysis regarding PV monitoring performances and short-term PV generation forecasting discrepancies.

Decentralized Energy Trading Systems for Multiple BEMS Based on Solar Power Generation Forecasting

Decentralized Energy Trading Systems for Multiple BEMS Based on Solar Power Generation Forecasting

Long Term Solar Power Generation Prediction using Adaboost as a Hybrid of Linear and Non-linear Machine Learning Model

The usage of renewable energy sources has increased manifolds in terms of electric utilities. From other non-conventional sources, solar energy has been seen as a promising and convenient source used around the globe. In terms of meeting the daily requirements, solar energy has huge potential to fulfil the world’s demand. However, firstly the characteristic of solar energy is very unpredictable and intermittent due to variation of weather. Secondly, the optimization and the planning of smart grid effect the operation of PV system. Thus, prediction on the long horizon is needed to address this problem. Nevertheless, long term forecasting of solar power generation is deliberated as a challenging problem. Therefore, this paper proposes a 10 day ahead solar power forecasting using combination of linear and non-linear machine learning models. At first, the outputs are generated from Recurrent Neural Network (RNN), Support Vector Machine (SVM) and Autoregressive with exogenous variable (ARX). Later on, these three outputs are combined and are made as a strong classifier with the Adaptive boost (Adaboost) algorithm. The simulations were conducted on the data obtained from real PV plant. By the experimental results and discussion, it was endogenously concluded that the combination of all techniques with the Adaboost have increased the performances and showing the high accuracy as compare to the individual machine learning models. The hybrid Adaboost shows %MAPE 8.88, which proven high accuracy. While on the other hand, for the individual technique, RNN shows 10.88, SVM reveals 11.78 and ARX gives 13.00 of percentage MAPE. The improvement proves that combination of techniques performs better than individual models and proclaims the high accuracy.

Key Operational Issues on the Integration of Large-Scale Solar Power Generation—A Literature Review

Solar photovoltaic (PV) power generation has strong intermittency and volatility due to its high dependence on solar radiation and other meteorological factors. Therefore, the negative impact of grid-connected PV on power systems has become one of the constraints in the development of large scale PV systems. Accurate forecasting of solar power generation and flexible planning and operational measures are of great significance to ensure safe, stable, and economical operation of a system with high penetration of solar generation at transmission and distribution levels. In this paper, studies on the following aspects are reviewed: (1) this paper comprehensively expounds the research on forecasting techniques of PV power generation output. (2) In view of the new challenge brought by the integration of high proportion solar generation to the frequency stability of power grid, this paper analyzes the mechanisms of influence between them and introduces the current technical route of PV power generation participating in system frequency regulation. (3) This section reviews the feasible measures that facilitate the inter-regional and wide-area consumption of intermittent solar power generation. At the end of this paper, combined with the actual demand of the development of power grid and PV power generation, the problems that need further attention in the future are prospected.

Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend.

The increased demand for solar renewable energy sources has created recent interest in the economic and technical issues related to the integration of Photovoltaic (PV) into the grid. Solar photovoltaic power generation forecasting is a crucial aspect of ensuring optimum grid control and power solar plant design. Accurate forecasting provides significant information to grid operators and power system designers in generating an optimal solar photovoltaic plant and to manage the power of demand and supply. This paper presents an extensive review on the implementation of Artificial Neural Networks (ANN) on solar power generation forecasting. The instrument used to measure the solar irradiance is analysed and discussed, specifically on studies that were published from February 1st, 2014 to February 1st, 2019. The selected papers were obtained from five major databases, namely, Direct Science, IEEE Xplore, Google Scholar, MDPI, and Scopus. The results of the review demonstrate the increased application of ANN on solar power generation forecasting. The hybrid system of ANN produces accurate results compared to individual models. The review also revealed that improvement forecasting accuracy can be achieved through proper handling and calibration of the solar irradiance instrument. This finding indicates that improvements in solar forecasting accuracy can be increased by reducing instrument errors that measure the weather parameter.