Forecasting Of Power Generation Research Articles

Effective LSTMs with seasonal-trend decomposition and adaptive learning and niching-based backtracking search algorithm for time series forecasting

Long short-term memory faces challenges in information mining and parameter selection due to inherent uncertainty and randomness. In this study, we propose a novel hybrid model called ‘STL-ALN_BSA-LSTM’ that aims to tackle this issue. In the proposed model, data is first prepossessed using seasonal-trend decomposition to extract informative features. Next, we propose an adaptive learning and niching-based backtracking search algorithm to enhance the robustness and learning ability of the original backtracking search algorithm, which affords a balance between exploration and exploitation. Subsequently, the weights and thresholds within each LSTM are optimized by our optimization algorithm automatically, leading to improved interpretability and accuracy of the model. Finally, the proposed optimization algorithm was evaluated using five benchmark functions, and the hybrid model was compared with sixteen baselines on two real-world datasets. For rainfall forecasting, the hybrid model achieved the root mean square error of 7.3772, mean absolute error of 2.6187, and R2 of 0.8388. For power generation forecasting, our model outperformed the second-best model by 29.6%, 34.4%, and 16 ‰, respectively, as in the above metrics. These results demonstrate that the proposed hybrid model can attain higher accuracy in real-life applications than previous models, which contributes in-depth insights into parameter optimization for time series forecasting, and provides valuable suggestions in resource planning, risk reduction, and decision-making. The core code of this work can be found from https://github.com/zjuml/STL-ALNBSA-LSTM.

Deep Learning for Variable Renewable Energy: A Systematic Review

In recent years, both fields, AI and VRE, have received increasing attention in scientific research. Thus, this article’s purpose is to investigate the potential of DL-based applications on VRE and as such provide an introduction to and structured overview of the field. First, we conduct a systematic literature review of the application of Artificial Intelligence (AI), especially Deep Learning (DL), on the integration of Variable Renewable Energy (VRE). Subsequently, we provide a comprehensive overview of specific DL-based solution approaches and evaluate their applicability, including a survey of the most applied and best suited DL architectures. We identify ten DL-based approaches to support the integration of VRE in modern power systems. We find (I) solar PV and wind power generation forecasting, (II) system scheduling and grid management, and (III) intelligent condition monitoring as three high potential application areas.

Accurate four-hour-ahead probabilistic forecast of photovoltaic power generation based on multiple meteorological variables-aided intelligent optimization of numeric weather prediction data

Accurate four-hour-ahead probabilistic forecast of photovoltaic power generation based on multiple meteorological variables-aided intelligent optimization of numeric weather prediction data

Comparison of machine learning and statistical methods in the field of renewable energy power generation forecasting: a mini review

In the post-COVID-19 era, countries are paying more attention to the energy transition as well as tackling the increasingly severe climate crisis. Renewable energy has attracted much attention because of its low economic costs and environmental friendliness. However, renewable energy cannot be widely adopted due to its high intermittency and volatility, which threaten the security and stability of power grids and hinder the operation and scheduling of power systems. Therefore, research on renewable power forecasting is important for integrating renewable energy and the power grid and improving operational efficiency. In this mini-review, we compare two kinds of common renewable power forecasting methods: machine learning methods and statistical methods. Then, the advantages and disadvantages of the two methods are discussed from different perspectives. Finally, the current challenges and feasible research directions for renewable energy forecasting are listed.

How do North American weather regimes drive wind energy at the sub-seasonal to seasonal timescales?

There has been an increasing need for forecasting power generation at the subseasonal to seasonal (S2S) timescales to support the operation, management, and planning of the wind-energy system. At the S2S timescales, atmospheric variability is largely related to recurrent and persistent weather patterns, referred to as weather regimes (WRs). In this study, we identify four WRs that influence wind resources over North America using a universal two-stage procedure approach. These WRs are responsible for large-scale wind and power production anomalies over the CONUS at the S2S timescales. The WR-based reconstruction explains up to 40% of the monthly variance of power production over the western United States, and the explanatory power of WRs generally increases with the increase of timescales. The identified relationship between WRs and power production reveals the potential and limitations of the regional WR-based wind resource assessment over different regions of the CONUS across multiple timescales.

A Review of Modern Wind Power Generation Forecasting Technologies

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

EMD-based gray combined forecasting model - Application to long-term forecasting of wind power generation

Wind power is the most promising renewable energy source after hydropower because of its mature technology and low price, and has great potential for carbon emission reduction. Long-term forecasts of its power generation can help power companies to develop operational plans, grid configuration and power dispatch, and can also provide a basis for the government to formulate energy and environmental policies. However, due to the characteristics of China's monsoon climate and wind power industry development, wind power generation data are characterized by nonlinear cycles and small data volume, which makes accurate prediction more difficult. To this end, this paper develops a new prediction model and applies it to the long-term prediction of wind power generation in China, and proposes some targeted policy recommendations based on the prediction results to promote the development of China's wind power industry.

Forecasting clean energy power generation in China based on a novel fractional discrete grey model with a dynamic time-delay function

Clean energy plays an essential role in responding to environmental crises. Accurate forecasting of clean energy power generation can provide necessary references for the formulation of energy policy. This paper proposes a novel fractional discrete grey model with a dynamic time delay function (DTDFF-DGM (1,1)) to forecast clean energy power generation. This model introduces the fractional accumulation operator and the dynamic time-delay function into the discrete grey model, which ensures the priority of new information in the original data and improves the model's adaptability to different sample data. The optimal parameters of the model are calculated by the dynamic linkage between the fitting error and the test error, which effectively avoids the overfitting problem. Empirical studies have proved that the model has better prediction accuracy compared to other methods. Finally, the proposed model is employed to forecast clean energy power generation in China. The results show that from 2020 to 2025, the overall growth rate of hydropower, wind power and nuclear power in China will be 9.27%, 119.61% and 36.38%, respectively. Based on the discussion of the forecast results, relevant policy suggestions were made. This paper realizes the transformation from a static model to a dynamic model in methodology and promotes the sustainable development of clean energy power generation in application.

Implementation of Digital Twin for Increasing Efficiency of Renewable Energy Sources

This paper presents an analysis of the instability of the electricity generation of renewable energy sources (RESs), specifically Digital Twins of RESs. The first part deals with the analysis of RES electricity generation around the world and Ukraine. The following chapter describes features of functioning power grids in modern conditions in Ukraine and ways to ensure the balance reliability in the power system for conditions of high-grade RES integration. The rapid increase in electricity generation RESs causes control problems of distributed power supply in the power grid. A mathematical model of the parameter controls in normal mode electric power systems for conditions with high integration of RESs is proposed in the second part. The study investigates components of the optimality criterion at the control of normal mode parameters of the electric power system with RESs. In general, digital transformation helps decarbonize the energy supply, decrease dependency on fossil fuels, and integrate renewables into power systems. A model Digital Twin (DT) of a photovoltaic system, or an exact 3D visualization, analyzing the accumulator system depending on load and generation, are presented. The problems of Digital Twin are very widely discussed, but many papers and studies are general without any practical implementations. The main part of this paper focuses on research and deals with daily electricity generation from different kinds of RESs, namely mini-hydropower stations, photovoltaic power stations, and wind power stations. Measured data of electricity generation from photovoltaic power plants, wind power plants, and mini-hydropower plants and obtained meteorological factors were used for the calculation of Spearman’s, Kendall’s, and Pearson’s correlation rank coefficients. The main contribution of this research is to determine the main metrological factors for each kind of studied RES. In the future, it will help to decide the task of forecasting power generation more presciently. Additionally, the presented model of DT RESs allows the installation and operation of grids with higher efficiency, because it can help to predict all influences, from shading up to the optimization of the battery storage system.

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.

Short-term PV power forecast using hybrid deep learning model and Variational Mode Decomposition

In recent decades, the dramatic transformation from conventional energy to renewables, such as photovoltaic (PV), has been extensively occurred to address the increasing electricity demand and environmental issues. Nevertheless, the operation of PV systems is adversely affected by the intermittence of meteorological factors, such as solar irradiance. Therefore, accurate PV power generation forecast has played a vital role to stimulate the integration of renewable energy sources into the power grid. This study proposes a novel scheme for short-term PV power forecast based on Transformer Neural Network (TransNN) integrated with Convolutional Neural Network (CNN). In addition, Variational Mode Decomposition (VMD) is adopted in the data pre-processing stage to enhance the forecast accuracy. The performance of the proposed model is validated utilizing two real-world datasets, and five benchmark models are dedicated to forecast outcome comparison. The predicted results indicated the superiority of the proposed model compared to the referenced models by achieving the Mean Absolute Error (MAE) values under 1 kW in all cases.

Predicting Power Generation from a Combined Cycle Power Plant Using Transformer Encoders with DNN

With the development of the Smart Grid, accurate prediction of power generation is becoming an increasingly crucial task. The primary goal of this research is to create an efficient and reliable forecasting model to estimate the full-load power generation of a combined-cycle power plant (CCPP). The dataset used in this research is a subset of the publicly available UCI Machine Learning Repository. It contains 9568 items of data collected from a CCPP during its full load operation over a span of six years. To enhance the accuracy of power generation forecasting, a novel forecasting method based on Transformer encoders with deep neural networks (DNN) was proposed. The proposed model exploits the ability of the Transformer encoder to extract valuable information. Furthermore, bottleneck DNN blocks and residual connections are used in the DNN component. In this study, a series of experiments were conducted, and the performance of the proposed model was evaluated against other state-of-the-art machine learning models based on the CCPP dataset. The experimental results illustrated that using Transformer encoders along with DNN can considerably improve the accuracy of predicting CCPPs power generation (RMSE = 3.5370, MAE = 2.4033, MAPE = 0.5307%, and R2 = 0.9555).

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.

Modeling of microbial fuel cell power generation using machine learning-based super learner algorithms

Electricity generation from microbial fuel cells (MFCs) is a potential environment-friendly technology. This study provides Bayesian Algorithm (BA) based Support Vector Regression (SVR) and Boosted Regression Tree (BRT) as prospective super learner modeling tools (BA-SVR, BA-BRT) for predictions of electricity production from MFCs. The membrane thickness, external resistance, and anode area were considered independent variables, while power generation was taken as a response variable. The key novelties of this study include (i) hybridization of BA with SVR and BRT (separately) for forecasting power generation from fuel cells for the first time, (ii) performance comparison of the developed models (BA-SVR and BA-BRT) with the existing Response Surface Methodology (RSM) based on the coefficient of determination (R2), relative error (RE), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and computing efficiency, and the (iii) analysis of the models’ robustness by utilizing Gaussian white noise. Based on the performance indicators, the proposed super leaner models showed excellent performance compared to the existing M.J. Salar-García et al. RSM model. The BA-SVR model provided the lowest errors (MAE of 2.94, RSME of 7.2926, MAPE of 13.8341) with the highest R2 of 0.9981, compared to the BA-BRT and RSM models. The proposed BA-SVR model showed superior performance to the RSM and BA-BRT models in predicting the MFCs’ power generation, with a performance improvement of more than 90% regarding MAPE, as an example. The future prediction and high robustness of the proposed super learner model would ensure quick estimation for maximization of electricity generation that may lead to reducing massive lab trials and saving resources.

AI 기반 재생에너지 발전 예측 기술 특허 동향

In order to utilize renewable power generation that contains uncertainty due to natural intermittence, it is essential to develop renewable power generation forecasting technologies. This paper focuses on identifying the technology development of artificial intelligence (AI)-based renewable power generation by the analysis of patents. In this study, AI-based renewable power generation forecasting technology is comprehensively defined, and related patent applications are systematically identified to analyze country, period, and technological trends. The results indicate that 1) renewable power generation forecasting has reached a technological maturity in Japan, the United States, and Europe, 2) to overcome this, AI technology has been being grafted onto renewable power generation forecasting, 3) the use of meteorological data was described as a major right, and AI technology was described as an applicable additional item due to the characteristics of AI technology patents.

A novel cyber-Resilient solar power forecasting model based on secure federated deep learning and data visualization

Improving the accuracy of photovoltaic (PV) power forecasting is crucial to ensure more effective use of energy resources. Improvements are especially important for regions for which historical solar radiation data do not exist. This paper proposes a cyber-secure forecasting model called federated deep learning (FDL) to forecast PV power generation in various regions across Iran. The training process in each client is done by a convolutional neural network (CNN). Then, a generalizable global supermodel is generated based on the features extracted in each client, which has the ability to generalize and forecast for regions where there is no training data. Preserve data privacy and ideal performance against cyber-attacks are prominent features of the proposed method. The use of the proposed method is illustrated with a case study for Iran. The proposed FDL network is designed with 9 clients and three different scenarios were developed to test the robustness of the suggested method. In the first scenario, the PV power generation forecasting is done via the proposed technique and other conventional methods. The performance accuracy (R2) of the generated global supermodel in this scenario for PV power generation forecasting in the regions of Khomein, Meybod, Varzaneh, Taleghan, and Shiraz are obtained as 0.981, 0.989, 0.986, 0.983, and 0.987 respectively. However, it was observed that other conventional deep learning-based models such as CNN and long short-term memory were not able to provide any forecasting for these regions. The second scenario models the scaling attack as a specific pattern of false data injection attack, to evaluate the performance of forecasting models against the data integrity attack. In the third scenario, cyber-attack detection is performed based on data visualization and image processing procedures. The results presented in different scenarios emphasize the high accuracy and generalizability of the global cyber-secure supermodel in PV power generation forecasting in different regions of Iran.

Short-term forecasting of wind power generation using artificial intelligence

As global warming is increasing due to conventional sources the government and the private sectors introduce policies to minimize it, renewable energy has been developed and deployed because of these strategies. Among the various renewable energy sources, wind energy is the fastest-growing and cleanest energy resource in the world. However, predicting wind power is not easy due to the nonlinearity in wind speed that eventually depends on weather conditions. To reduce these issues improved forecasting models have been used to get the correct results and improve the performance and stability of the power system and thereby its reliability and security.In this work, two models are used to predict the “Output of Wind Turbine” to improve the prediction accuracy of short-term wind power generation. The two models namely the Gated Recurrent Unit (GRU) from the deep learning model and Autoregressive Integrated Moving Average (ARIMA) from Statistical Learning. The data used in this research is collected from the wind power plant, Located in Jhimpir Pakistan. This study compares the accuracy metrics of deep learning models and statistical models to determine which model is the most effective for producing wind power.The results are obtained by using python programming in Jupyter Notebook software and the accuracy metrics of each algorithm are compared with each other as a result Gated recurrent unit (GRU) is the best model among others with the least possible errors and high accuracy. i.e., up to 0.047 root mean square error, 0.89 coefficient of metrics, and 0.03 mean absolute error. Hence, due to its advanced features, then other deep learning, and statistical models the Gated recurrent unit (GRU) Model is suitable for the prediction of wind turbine output power.

Machine learning based renewable energy generation and energy consumption forecasting

ABSTRACT Renewable energy generation sources are the need of the hour to mitigate the challenges of fossil fuels, environmental impacts, large transmission and distribution losses, and variable consumption pattern. Renewable energy such as solar PV generation or wind generation is most unpredicted generation and depends upon weather constraints. A promising prediction of generation is a suitable solution for this problem. A machine learning-based conventional neural network algorithm is proposed to forecast the generation and calculate the errors in generation forecast. The generated energy is supplied to consumers, but the consumers consume electricity by their choice and an energy management model is required at the consumer end to supply the available predicted generation to develop a self-sustainable microgrid. The energy consumption at consumer end is essential to predict, and based on the predicted consumption at hourly bases, the cost model for the consumers is developed. The consumption profile is forecasted using long short-term memory as it is a good tool for time-series forecasting using available sequential load consumption data. The demand is forecasted and analyzed for selected load categories (i.e. fixed, non-shift-able, and shift-able loads). The evaluation matrix mean square error (MSE), mean absolute error, and root mean square error (RMSE) are used for the error calculations. The RMSE for solar and wind power generation forecasting have been 2.472 and 3.034, respectively. The RMSE for fixed, non-shiftable, and shiftable demand forecasting have been 0.027, 0.067, and 0.21, respectively. The results show that the proposed methods for predictions of solar, wind power generation and energy consumption forecast achieve better accuracy. The presented methodology can be implemented with energy storage for renewable energy production and demand management for energy from sustainable energy resources.

Wind Forecast at Medium Voltage Distribution Networks

Due to the intermittent and variable nature of wind, Wind Power Generation Forecast (WPGF) has become an essential task for power system operators who are looking for reliable wind penetration into the electric grid. Since there is a need to forecast wind power generation accurately, the main contribution of this paper is the development, implementation, and comparison of WPGF methods in a framework to be used by distribution system operators (DSOs). The methodology applied comprised five stages: pre-processing, feature selection, forecasting models, post-processing, and validation, using the historical wind power generation data (measured at secondary substations) of 20 wind farms connected to the medium voltage (MV) distribution network in Portugal. After comparing the accuracy of eight different models in terms of their relative root mean square error (RRMSE), extreme gradient boosting (XGBOOST) appeared as the best-suited forecasting method for wind power generation. The best average RRMSE achieved by the proposed XGBOOST model for 1-year training (January–December of 2020) and 6 months forecast (January–June of 2021) corresponds to 13.48%, outperforming the predictions of the Portuguese DSO by 20%.

Ensemble Machine-Learning Models for Accurate Prediction of Solar Irradiation in Bangladesh

Improved irradiance forecasting ensures precise solar power generation forecasts, resulting in smoother operation of the distribution grid. Empirical models are used to estimate irradiation using a wide range of data and specific national or regional parameters. In contrast, algorithms based on Artificial Intelligence (AI) are becoming increasingly popular and effective for estimating solar irradiance. Although there has been significant development in this area elsewhere, employing an AI model to investigate irradiance in Bangladesh is limited. This research forecasts solar radiation in Bangladesh using ensemble machine-learning models. The meteorological data collected from 32 stations contain maximum temperature, minimum temperature, total rain, humidity, sunshine, wind speed, cloud coverage, and irradiance. Ensemble machine-learning algorithms including Adaboost regression (ABR), gradient-boosting regression (GBR), random forest regression (RFR), and bagging regression (BR) are developed to predict solar irradiance. With the default parameters, the GBR provides the best performance as it has the lowest standard deviation of errors. Then, the important hyperparameters of the GRB are tuned with the grid-search algorithms to further improve the prediction accuracy. On the testing dataset, the optimized GBR has the highest coefficient of determination (R2) performance, with a value of 0.9995. The same approach also has the lowest root mean squared error (0.0007), mean absolute percentage error (0.0052), and mean squared logarithmic error (0.0001), implying superior performance. The absolute error of the prediction lies within a narrow range, indicating good performance. Overall, ensemble machine-learning models are an effective method for forecasting irradiance in Bangladesh. They can attain high accuracy and robustness and give significant information for the assessment of solar energy resources.