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Wind prediction technology has been the focus on the national research with the basis of the power system planning, a reference for power dispatch, and the optimal power flow distribution. Prediction technology is moving into the direction of controlling refinement with the development of the information technology, the artificial intelligence technology, and the improvement of edge computing devices. The wind farms can use real-time wind power prediction to improve their overall efficiency through advanced planning of the wind turbine adjustment and the pre-setting of the yaw, pitch, and generation excitation control systems. This paper proposes an innovative autoregressive dynamic adaptive (ARDA) model based on the improvement of the autoregressive (AR) model. The fixed parameter estimation method of the AR model is improved in the proposed model to a dynamically adaptive stepwise parameter estimation method. Meanwhile, the coefficients of the model are updated adaptively based on the characteristics of wind power data, which improves the accuracy of the proposed model. The prediction accuracy of the proposed model is further improved by the residual function. It was observed that the model adapts well to wind power data with different degrees of volatility. The ARDA model and two other models were tested by using stationary and fluctuating wind power data (unit: seconds), and the wind power prediction results at different forecasting step lengths were compared. It was observed that the ARDA model is more accurate, with faster calculation rate, and better dynamic adaptability to data fluctuations than the ARIMA and LSTM models. This paper proposes an important method for real-time power prediction that can be employed for the advanced control and improved the power generation of wind farms. • We develop a novel model called the ARDA model based on AR model. • The proposed model is self-learning and dynamically adaptive to data fluctuations. • Parameter estimation highlights neighbouring data and forgets distant data. • The residual function corrects the prediction result based on data fluctuations. [ABSTRACT FROM AUTHOR]

Wind power integration and carbon reduction cooperation, as low-carbon approaches, are introduced into the current power dispatching process under carbon reduction requirements. In order to measure the environmental performance of power dispatch influenced by the above approaches, this paper firstly presents a low-carbon power dispatch model for wind power integrated system imported with carbon reduction cooperation. Next, the methods related to wind speed prediction, optimization algorithm and efficiency evaluation are fully discussed. Finally, empirical analysis shows that: (1) the overall effect of ARIMA-NARX model is superior to that of ARIMA model for wind speed prediction, whose MSE is reduced by 79.73%, (2) compared with GA and PSO, GA-PSO has better effect of optimization, which can effectively jump out of the local optimal value, and its convergence speed keeps improving, (3) the two low-carbon approaches can facilitate the achievement of system carbon reduction targets with good environmental performance of power dispatch, and (4) low-carbon power dispatching strategies taking full account of these two approaches are developed, which proves to be of reliability, economy and environment. • A power dispatching model integrated with two low-carbon approaches is proposed. • ARIMA-NARX hybrid model with good effect of wind speed prediction is constructed. • GA-PSO with better optimization effect is designed. • Environmental performance of low-carbon power dispatch is measured. • Power dispatching strategies developed are of reliability, economy and environment. [ABSTRACT FROM AUTHOR]

Short-term time series wind power predictions are extremely essential for accurate and efficient offshore wind energy evaluation and, in turn, benefit large wind farm operation and maintenance (O&M). However, it is still a challenging task due to the intermittent nature of offshore wind, which significantly increases difficulties in wind power forecasting. In this paper, a novel hybrid model, using unique strengths of Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Deep-learning-based Long Short-Term Memory (LSTM), was proposed to handle different components in the power time series of an offshore wind turbine in Scotland, where neither the approximation nor the detail was considered as purely nonlinear or linear. Besides, an integrated pre-processing method, incorporating Isolation Forest (IF), resampling, and interpolation was applied for the raw Supervisory Control and Data Acquisition (SCADA) datasets. The proposed DWT-SARIMA-LSTM model provided the highest accuracy among all the observed tests, indicating it could efficiently capture complex times series patterns from offshore wind power. [ABSTRACT FROM AUTHOR]

Waste-to-energy (WTE) technologies convert municipal solid, and biomass wastes into affordable renewable heat and power energy. However, there are large uncertainties associated with using waste feed as a renewable energy source. This paper proposes a WTE management tool that provides forecasting and real-time optimization of power generated with the consideration of anomaly. The WTE management framework was designed based on a biological neural network, the Hierarchical Temporal Memory (HTM) coupled with a dual-mode optimization procedure. The HTM model is inspired by the mechanism in the cerebral neocortex of the brain, providing anomaly identification and spatial-temporal prediction. In this work, the HTM-based smart energy framework is demonstrated in an industrial case study for the power generation of a waste-to-energy cogeneration system. HTM was compared with methods such as Long Short-Term Memory (LSTM) neural network, Autoregressive Integrated Moving Average (ARIMA), Fourier Transformation Extrapolation (FTE), persistence forecasting, and was able to achieve mean squared error (MSE) of 0.08466% while giving 35450 Euro profit in half a year. Coupled with a novel dual-mode optimization procedure, HTM demonstrated 11% improvement with respect to only predictive optimization (with HTM) in estimated gross profit. [Display omitted] • A practical, effective and robust smart energy management framework was presented. • A neocortex-inspired algorithm, Hierarchical Temporal Memory was used for prediction. • An industrial cogeneration plant in Czech Republic was used as the case study. • HTM predicted demands better than LSTM, ARIMA and Fourier analysis (MSE = 0.8466%). • Novel dual-mode optimization was used which achieved 27.3% improvement in gross profit. [ABSTRACT FROM AUTHOR]

Identifying the research topics of China's energy transition policy and predicting future research trends are crucial for policymakers to make informed decisions and advance the energy transition. This study applied the Latent Dirichlet Allocation (LDA) to extract 26 major research topics from the 13,976 abstracts of Chinese policy research papers in web of science (WOS), conceptualized the five most popular topics and used the ARIMA model to predict trends over the next 36 months. The results show that except for Economic and Environmental Impact of Renewable Energy Consumption (Topic13), which will first experience unanticipated fluctuations and then show strong growth capacity, all other topics will continue to remain positive growth trend. Research and Development (Topic3) is driven by the urgency of China's energy transformation. Economic and Environmental Impact of Renewable Energy Consumption (Topic13) is driven by the potential for the development of renewable energy. Economic Impact of Climate Change (Topic26) is driven by the climate change debate. Economic Growth, Urbanization, and Energy Consumption (Topic7) is driven by a discussion of their relationship and the mechanisms influencing them. Regional Efficiency and Productivity Analysis (Topic19) is driven by regional differences in China's energy policies. [ABSTRACT FROM AUTHOR]

Wind speed forecasting is a challenging task, especially medium-term forecasting on 10 min sampling interval, due to the intermittent nature of wind. Several numerical methods were presented to improving wind speed forecasting accuracy, being the decomposition strategy reported as the key step to improve forecasting results, where the raw wind speed data is first decomposed into several patterns and then recomposed in a new time series. This can effectively reduce the uncertainty in the wind speed forecasting, leading us to believe that the precision of the forecast has a strong correlation with the preprocessing strategy, rather than with the forecasting technique. Based on this understanding, this paper combines two decomposition techniques, amplitude and frequency modulation-demodulation signal theory. The proposed decomposition approach is further combined with the parametric forecasting model named AutoRegressive Integrated Moving Average (ARIMA), resulting in a new ensemble learning method called Variational Mode Decomposition-Singular Spectral Analysis-AutoRegressive Integrated Moving Average (VMD-SSA-ARIMA). All forecasting results obtained by this ensemble are compared with those from the ARIMA. The ensemble results demonstrated stabilization of the forecasting errors. This indicates the ability of the proposed hybrid approach to decompose wind speed time series into uncorrelated components, reducing the errors from one up to a 12-steps-ahead forecasting horizon. Image 1 • New hybrid model for wind speed time series decomposition is proposed. • The AM-FM theory combined with an ensemble of VMD and SSA-ARIMA is an original idea on context of wind speed forecasting. • The proposed ensemble is capable of extracting the most significant information related to wind speed time series. • The model presents excellent results with stable performance. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) can play a significant role in economic growth. This article examines the development of the wind energy market in the EU. The applicable regulations, directives, and legal acts are reviewed, and the State's role in the development of the renewable energy market is discussed. The analyses were carried out based on the available literature and Eurostat data. The authors of the paper used basic statistics to summarize the changes in wind energy and make a prognosis. The data analysis uses the Autoregressive Integrated Moving Average Method (ARIMA). The survey found that the highest average wind cumulative installed capacities were in Germany (31026 MW), Spain (18602 MW), United Kingdom (7894 MW) and France (6108 MW). Our analysis indicates that the biggest average annual installed wind capacity was in Germany (2952.6 MW), Spain (1224.5 MW) and the United Kingdom (1363.8 MW). These same countries achieved the highest average wind gross electricity production in the years 2004–17: Germany (50.9 TWh), Spain (39.3 TWh) and United Kingdom (18.9 TWh). The study finds that wind cumulative installed capacity will increase in most countries of the EU. • The European Union onshore wind energy market seems to be stable. • Wind power plays an important role as a source of energy supply. • The most important wind gross electricity producers in the EU are: Germany and Spain. • The highest increase of wind cumulative installed capacity in 2022 will be in Croatia. [ABSTRACT FROM AUTHOR]

Solar forecasting has evolved towards becoming a key component of economical realization of high penetration levels of photovoltaic (PV) systems. This paper presents two novel stochastic forecasting models for solar PV by utilizing historical measurement data to outline a short-term high-resolution probabilistic behavior of solar. First, an uncertain basis functions method is used to forecast both solar radiation and PV power. Three possible distributions are considered for the uncertain basis functions - Gaussian, Laplace, and Uniform distributions. Second, stochastic state-space models are applied to characterize the behaviors of solar radiation and PV power output. A filter-based expectation-maximization and Kalman filtering mechanism is employed to recursively estimate the system parameters and state variables. This enables the system to accurately forecast small as well as large fluctuations of the solar signals. The introduced forecasting models are suitable for real-time tertiary dispatch controllers and optimal power controllers. The PV forecasting models are tested using solar radiation and PV power measurement data collected from a 13.5 kW PV panel installed on the rooftop of our laboratory. The results are compared with standard time series forecasting mechanisms and show a substantial improvement in the forecasting accuracy of the total energy produced. • Short-term high-resolution probabilistic behavior of solar is investigated. • Two novel stochastic forecasting models are proposed for solar PV. • Forecasting of solar irradiance and PV power is provided. • The proposed algorithm performs better than traditional ARIMA model. [ABSTRACT FROM AUTHOR]

Electric vehicle charging demand is highly variable depending on the charging pattern of consumers. If electric vehicle charging time is converged, the charging demand connected to power grids will increase and system instability may be induced. Wind Generating Sources (WGRs) can provide as much of the charging energy as possible. In this paper, we propose a simultaneous approach implementing wind-powered electric vehicle charging stations in order to distribute the charging demand of the electric vehicle with wind generating resources. The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model is applied to predict hourly wind power outputs. The augmented ARIMAX model is extended from Autoregressive Integrated Moving Average (ARIMA) model by adding exogenous such as a wind speed including grid integration analysis simulation processes. To validate the proposed approach for electric vehicle charging dispersion, we use the empirical data from the Jeju Island's wind farms in South Korea. [ABSTRACT FROM AUTHOR]

A very short-term solar PV power generation forecast can be extremely helpful for real-time balancing operation in an electricity market which in turn will profit both energy suppliers as well as customers. However, the intermittency of solar PV power introduces inaccuracies in its forecast. To address this challenge, the research paper has studied the effect of wavelet decomposition of solar PV power time series on its forecast. A novel and time adaptive, Seasonal Autoregressive Integrated Moving Average (SARIMA)-Random Vector Functional Link (RVFL) neural network hybrid model assisted by Maximum Overlap Discrete Wavelet Transform (MODWT) has been proposed. The solar PV power generation data obtained from roof-top solar PV plants installed at IIT Gandhinagar is used to develop and validate the forecast models. Various numerical forecast accuracy measures have been calculated which show an improvement in accuracy and adaptability of proposed forecast model over constituent models. • Wavelet decomposition negatively affects solar PV power forecast in clear sky weather. • Wavelet decomposition aids the forecast model in cloudy/rainy sky weather. • Combination of linear and non-linear models perform better than individual ones. • Time adaptive nature of model combination parameter increases accuracy. [ABSTRACT FROM AUTHOR]

Abstract: In this paper the analysis and forecasting of wind velocities in Chetumal, Quintana Roo, Mexico is presented. Measurements were made by the Instituto de Investigaciones Eléctricas (IIE) during two years, from 2004 to 2005. This location exemplifies the wind energy generation potential in the Caribbean coast of Mexico that could be employed in the hotel industry in the next decade. The wind speed and wind direction were measured at 10m above ground level. Sensors with high accuracy and a low starting threshold were used. The wind velocity was recorded using a data acquisition system supplied by a 10W photovoltaic panel. The wind speed values were measured with a frequency of 1Hz and the average wind speed was recorded considering regular intervals of 10min. First a statistical analysis of the time series was made in the first part of the paper through conventional and robust measures. Also the forecasting of the last day of measurements was made utilizing the single exponential smoothing method (SES). The results showed a very good accuracy of the data with this technique for an α value of 0.9. Finally the SES method was compared with the artificial neural network (ANN) method showing the former better results. [Copyright &y& Elsevier]

Simple linear methods are widely used for time series modelling and prediction and in particular for the forecast of wind speed variations. Linear prediction models are popular for their simplicity and computational efficiency, but their prediction accuracy generally deteriorates beyond a few time steps. In this paper we demonstrate that the prediction accuracy of simple auto-regressive (AR) models can be significantly improved, by as much as 60.15% for day-ahead predictions and up to 18.25% for week-ahead predictions, when combined with suitable time series decomposition. The comparison with new reference forecast model (NRFM) also shows similar accuracy gain of week ahead predictions. The combined model is capable of forecasting wind speed up to 7 days ahead with an average root mean square error less than 3 m/s. We also compare the performance of AR and f-ARIMA models in wind speed prediction and observe that the f-ARIMA model is no better than the AR model when used in combination with time series decomposition. [ABSTRACT FROM AUTHOR]

Abstract: The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (ARMA) model. While ANN by its non-linear nature is effective to predict cloudy days, ARMA techniques are more dedicated to sunny days without cloud occurrences. Thus, three hybrids models are suggested: the first proposes simply to use ARMA for 6 months in spring and summer and to use an optimized ANN for the other part of the year; the second model is equivalent to the first but with a seasonal learning; the last model depends on the error occurred the previous hour. These models were used to forecast the hourly global radiation for five places in Mediterranean area. The forecasting performance was compared among several models: the 3 above mentioned models, the best ANN and ARMA for each location. In the best configuration, the coupling of ANN and ARMA allows an improvement of more than 1%, with a maximum in autumn (3.4%) and a minimum in winter (0.9%) where ANN alone is the best. [Copyright &y& Elsevier]

Abstract: Wind speed forecasting is one of the most important technologies to guarantee the wind energy integrated into the whole power system smoothly. In this paper a hybrid model named EMD–ANN for wind speed prediction is proposed based on the Empirical Mode Decomposition (EMD) and the Artificial Neural Networks (ANNs). To choose the best training algorithm for the ANN model, several experimental simulations with different training algorithms are made. To estimate the performance of the EMD–ANN model, two forecasting cases are completed and the results are both compared with the ANN model and the Autoregressive Integrated Moving Average (ARIMA) model respectively. To avoid the randomness caused by the ANN model or the ANN part of hybrid EMD–ANN model, all simulations in this study are repeated at least 30 times to get the average. The results show that: (1) the performance of the proposed model is highly satisfactory; and (2) the proposed EMD–ANN hybrid method is robust in dealing with jumping samplings in non-stationary wind series. [Copyright &y& Elsevier]

Abstract: In this paper the wind speed forecasting in the Isla de Cedros in Baja California, in the Cerro de la Virgen in Zacatecas and in Holbox in Quintana Roo is presented. The time series utilized are average hourly wind speed data obtained directly from the measurements realized in the different sites during about one month. In order to do wind speed forecasting Hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models and Artificial Neural Network (ANN) models were developed. The ARIMA models were first used to do the wind speed forecasting of the time series and then with the obtained errors ANN were built taking into account the nonlinear tendencies that the ARIMA technique could not identify, reducing with this the final errors. Once the Hybrid models were developed 48 data out of sample for each one of the sites were used to do the wind speed forecasting and the results were compared with the ARIMA and the ANN models working separately. Statistical error measures such as the mean error (ME), the mean square error (MSE) and the mean absolute error (MAE) were calculated to compare the three methods. The results showed that the Hybrid models predict the wind velocities with a higher accuracy than the ARIMA and ANN models in the three examined sites. [Copyright &y& Elsevier]

Abstract: With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed/power forecasts. Accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the day-ahead electricity market. This paper examines the use of fractional-ARIMA or f-ARIMA models to model, and forecast wind speeds on the day-ahead (24h) and two-day-ahead (48h) horizons. The models are applied to wind speed records obtained from four potential wind generation sites in North Dakota. The forecasted wind speeds are used in conjunction with the power curve of an operational (NEG MICON, 750kW) turbine to obtain corresponding forecasts of wind power production. The forecast errors in wind speed/power are analyzed and compared with the persistence model. Results indicate that significant improvements in forecasting accuracy are obtained with the proposed models compared to the persistence method. [Copyright &y& Elsevier]

Abstract: Comparison of two techniques for wind speed forecasting in the South Coast of the state of Oaxaca, Mexico is presented in this paper. The Autoregressive Integrated Moving Average (ARIMA) and the Artificial Neural Networks (ANN) methods are applied to a time series conformed by 7 years of wind speed measurements. Six years were used in the formulation of the models and the last year was used to validate and compare the effectiveness of the generated prediction by the techniques mentioned above. Seasonal ARIMA models present a better sensitivity to the adjustment and prediction of the wind speed for this case in particular. Nevertheless, it was shown both developed models can be used to predict in a reasonable way, the monthly electricity production of the wind power stations in La Venta, Oaxaca, Mexico to support the operators of the Electric Utility Control Centre. [Copyright &y& Elsevier]