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The alternation between cloudy and clear skies alters the photovoltaic production. This makes it necessary to anticipate these disturbances hours in advance for the correct operation of the electricity distribution plants and networks. In this paper, two short-term forecasting models (3 h) are developed to forecast the photovoltaic production in an integrated plant in the CIESOL building of the University of Almería. The methodology used is based on sky camera images and Artificial Intelligence techniques. Two models have been developed and compared applying artificial neural network (ANN) and support vector machine (SVM) techniques. The global irradiance predicted using sky camera images is used as an input variable in both models. In addition, the operational status of the plants has been included as an input parameter through the performance ratio. The results have shown that the errors made by ANN and SVM are very similar. For all sky conditions, the uncertainty of the production forecast differs by less than 2% from the uncertainty of the solar resource, which is the main source of error in the production models developed. [ABSTRACT FROM AUTHOR]

With the applications of Machine Learning (ML) in condition monitoring (CM) of wind turbines (WTs), regression-based approaches are mainly for WT power curve modelling to evaluate its performance. A fitted performance curve (PC) of power versus wind speed is prevailing and straightforward for WT performance monitoring. As WT operations could be affected by multiple parameters. It is difficult to identify the fault type merely by a power curve. Particularly at the rotor side of doubly fed induction generator (DFIG), there are miscellaneous electrical disturbances that have their distinct effects on current signals. Hence, by merely processing the rotor currents, a PC of standard deviation of currents versus wind speed can be employed for CM of rotor-side faults. A non-parametric regression model, Gaussian process regression (GPR), is used to predict the PC under normal operation, and afterwards, Fuzzy Inference System (FIS) is applied for fault diagnosis. Therefore, the paper proposes a hybrid GPR and FIS approach towards condition monitoring (CM) and fault diagnosis at the rotor side of a DFIG by merely processing rotor currents through various signal processing techniques, where miscellaneous electrical disturbances can be recognized and localized. The comparative results show that the hybrid approach has superiority over two common ML classifiers, support vector machine (SVM) and decision tree (DT), for fault diagnosis under both test cases including drift faults and excluding drift faults. By disregarding drift faults, this hybrid approach is validated to be efficient for a real-time application with overall promising performance metrics in terms of accuracy (98.29%), dependability (98.12%), and security (100%). The diagnosed results on individual faults and research shortcomings are also discussed. [ABSTRACT FROM AUTHOR]

Due to the inherent uncertainty and the mismatch between renewable energy output and load demand, renewable energy-based system optimization is increasingly important in China. Therefore, electricity demand response (EDR) is critical to the stability and efficiency of an integrated renewable energy system (IRES). The customers' demand response under the time-of-use (TOU) mechanism is the key to the entire process. However, how to optimize integrated renewable energy system based on accurately identifying the electricity demand response participation of customers is one of the main issues at this stage. To solve the problem, this paper presents a novel approach for integrated renewable energy system optimization considering electricity demand response management and multistage energy storage systems from the perspective of Fujian Province, China. To enable large-scale renewable energy integration, the original output is processed using the Hampel identifier (HI)-Savitzky-Golay filter (SGF) technology. A machine learning (ML)-based identification model is developed for EDR to determine whether the customers are participating or not. Driven by the time-of-use mechanism, customers can make dynamic adjustments based on the Logistics function. After that, an integrated renewable energy system model optimization strategy is proposed based on satisfying customers' electricity demand response. Throughout the optimization process, the multistage energy storage system plays a vital role in the residual fluctuation absorption for renewable energy filtering, the dynamic adjustment process of the demand response, and the integrated renewable energy system optimization. Through extensive case studies and discussions, it is demonstrated that random forest (RF) and support vector machine (SVM) reach 80 % identification accuracy with a 20 % testing ratio. According to the optimization approach proposed in this paper, the minimum operation cost can be calculated to be 9.336*106 CNY. The methodology presented in this paper establishes a special relationship between electricity demand response identification and dynamic adjustments, and provides an optimization model for Fujian Province, China, which is a reliable reference for future work. [ABSTRACT FROM AUTHOR]

Wind energy presents a high growth potential in the EU as an emission reduction strategy and to achieve the climate neutrality goal by 2050. Wind farms suitability analysis is one of the primary goals in the spatial planning of wind energy developments. This research paper introduces a hybrid spatial multicriteria GIS-based framework that combines Analytic Hierarchy Process (AHP), PROMETHEE II and Machine Learning algorithms to determine and predict the most efficient onshore wind farm locations by generating suitability index mappings. The methodology allows to overcome PROMETHEE II limitations in raster driven suitability analysis, utilizing machine learning regression methods as the k Nearest Neighbor and Support Vector Machines to predict a graduating mapping of suitability index for wind farm locations in northeastern Greece. The best configured models presented a RMSE of 0.0344 and 0.0154 respectively, indicating a quite high predictive performance. Suitability results indicate that 56.10 % of the feasible locations in the Thrace area present a positive outranking character for the kNN model and 56.79 % for the SVR model. The proposed framework, enriched by PROMETHEE II capabilities, assists energy and spatial planners in identifying suitable sites for wind farm siting and enables rational decision making that enhances efficient wind energy investments. [ABSTRACT FROM AUTHOR]

To achieve a high penetration of renewable energy integration, an effective solution is to explore the interdependence between numerical weather prediction (NWP) data and historical wind power to improve prediction accuracy. This paper proposes a novel combined approach for wind power prediction. The characteristics of NWP and historical wind power data are extracted by using the feature extraction technique, the predictor is designed based on extreme learning machine (ELM) and least squares support vector machine (LSSVM) model, and then key parameters of the prediction models are optimized by improving cuckoo search (ICS) to obtain a reliable value, which is defined as the pre-combined prediction value (PPA). To obtain a reliable result, a variance strategy is developed to allocate the weights of the pre-combined prediction model to obtain the final predicted values. Four seasons dataset collected from regional wind farms in China is utilized as a benchmark experiment to evaluate the effectiveness of the proposed approach. The results of comprehensive numerical cases with different seasons show that the proposed approach, which considers multiple-error metrics, including error metrics, accuracy rate, qualification rate, and improvement percentages, achieves higher accuracy than other benchmark prediction models. • A novel combined framework is proposed to predict short-term wind power. •Linear and nonlinear feature extraction strategies are applied to analyze the character of NWP data. •A variance combined strategy is used to allocate the optimal weight of the primary combined model. •The wind power prediction error analysis is conducted by multiple-error performance metrics. [ABSTRACT FROM AUTHOR]