When a PV system is connected to the grid, its **power** fluctuation may destabilize the grid and pose a threat to network security, which makes it even harder to formulate generation plans. As such, an accurate **prediction** of PV **power** output is required to make better generation plans, support the spatial and temporal compensation, and achieve coordinated **power** control, so that the need for energy storage capacity and operating costs can be reduced [ 4 ]. Moreover, better **prediction** of PV **power** also helps to enhance system security and stability, as well as optimize the operation of the **power** system [ 5 ]. The **prediction** methods include physical methods and statistical methods. Usually, a physical method first predicts the factors which directly influence the PV **power** output (such as solar radiation and ambient temperature) and then uses the forecast result as the input of the physical **model** to obtain the output **power**. On the other hand, a statistical method uses historical data to build

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error margin, each connection weights and the value of each unit are changed in direction of straight line from output layer to input layer. In this paper, Levenberg- Marquardt **algorithm** was adopted for updating each connection weight of unit [10]. The inertia and learning coefficient are the parameters of NN. The inertia pro- motes learning speed acts rapidly by changing each con- nection weights of neurons. The learning coefficient is explained, this parameter is preferred to large. At this time, it is necessary to stable the least square error mar- gin of NN **model**. The authors decide these parameters by trial-and-error method [5,6]. The effective learning has been improved by multiplying the learning coeffi- cient by the learning increase rate and the learning de- crease rate, and then variable of least square error margin is adjusted. Moreover, optimum number of hidden-layer neurons is decided to minimize the output error of NN by simulation result with using the training data [9].

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A medium-**term** solar irradiance forecasting **model** was developed in (Mar- quez and Coimbra, 2011) adopting predicted meteorological variables from the US National Weather Service’s (NWS) forecasting database as inputs to an Artificial Neural Network (AN N ) **model**. The inputs involved are the same from a validated forecasting **model** so mean bias error (M BE), root mean square error (RM SE) and correlation coefficient (R2) comparisons between the more established fore- casting **model** and the proposed one are included. A set of criteria for selecting relevant inputs was developed, input variables were selected using a version of the Gamma test combined with a genetic **algorithm**. The solar geotemporal variables were found to be critically important, while the most relevant meteorological vari- ables included sky cover, probability of precipitation, and maximum and minimum temperatures. Using the relevant input sets identified by the Gamma test, the de- veloped forecasting models improve RM SE for GHI by 10–15% over the reference **model**. rRM SE range from 15% to 22% for different models constructed on 13 month data set for same-day forecasts of GHI.

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As wind integration grows dramatically, the requirements for solving various problems, which include competitive **power** quality, **power** system stability and reliability, transmission capacity upgrades and standards of interconnection, become more challenging. However, improved wind **power** predicting can be considered as one of the most efficient ways to overcome many of these problems. Hence, the improvement of the performance of WPP tool has significant economic and technical impact. In this paper, a new predicting method **based** on SVM is proposed. There are three differences between the proposed method and the conventional SVM approach: 1) LS SVM was used instead of QP-SVM, 2) In order to improve the performance of this **algorithm**, a wavelet kernel is used instead of conventional kernel function as RBF. 3) The main parameter of this function is determined by PSO. History wind **power** data translate into time series predicting. WPP–PSO-LS-WSVM is effective tools to maximize the **power** captured thus increasing the reliability of wind **power** for wind farms. The **prediction** method for wind **power** **based** on PSO- LS-WSVM is a valid method to predict wind **power**. The PSO-LS-WSVM **model** accurate predict of wind **power** with a look-ahead time of up to 24 h. The final results of the test systems **based** on 2.5 MW wind turbine verified the feasibility and effectiveness of the proposed method to predict wind **power** series. Comparison with other methods proved that the proposed method is competitive in terms of dealing with wind **power** predicting.

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different wind **power** output even at the same wind speed. A wind farm comprises tens or even hun- dreds of turbines, which making the relationship be- tween the farm output and speed much weaker than that of a wind turbine. Even so, the wind **power** output depends on wind speed obviously, as shown in Fig. 3. The object of modeling an **ELM** is to characterize such kind of implicit dependence. However some anomalous data exists in the original datasets, which will have negative influence on the wind **power** forecasting accuracy. Two kinds of anomalies are supposed to be eliminated before building an **ELM** **model**.

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The **prediction** method **based** on similar date has achieved good results, but only the similarity analysis is made for the meteorological factors of the forecast day, and the effect of the **power** change trend on the wind **power** **prediction** is not taken into account. Ding Zhiyong Considering the similarity and continuity of wind speed, a SVM wind **power** forecasting method **based** on continuous period clustering is proposed [3]. But the length of the continuous period is set to half a month, too long to reflect the changes between historical and forecast data. According to the concept of "trend similarity", we select the similarity date. Although we consider the change of **power** curve, we do not analyze the relationship between forward change and backward trend. The selection of the trend k did not give a reasonable analysis [4]. Zhang Yiyang [5] divided similar day into "similar period". Firstly, looking for the similar wind **power** curve 12 hours before **prediction** time as a " reference period", and then find the similar wind **power** curve 12 hours after **prediction** time as " forecast section", to achieve the **prediction** in different level. However, when the **model** is established, only the correspondence between the similar daily powers is taken into account, and the influence of the reference **power** curve and the meteorological eigenvalue is not taken into account.

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This paper presents a forecasting **model** optimized by the DEPSO technique used for **short**-**term** PV **power** output forecasting of a PV system stationed at Deakin University (Victoria, Australia). DEPSO is a new metaheuristic swarm-**based** **algorithm** that efficiently and rapidly addresses global optimization problems. The stochastic nature of the DEPSO **algorithm** makes the system purely independent of its **power** output. Furthermore, the existence of the randomness of the system in the search process keeps the metaheuristic nature of the **algorithm** robust, reliable, efficient, and straightforward for **short**-**term** **power** forecasting. The limitations of the DE and PSO algorithms, such as the slow convergence rate of PSO and the lack of randomness in DE, are adequately addressed in the hybrid DEPSO technique. The comparison made among the DE, PSO, and DEPSO algorithms proves that the combinational evolutionary **algorithm** outperforms the two algorithms. The RMSE, MAE, MBE, VAR, WME, and MRE values of the forecasting **algorithm** are reduced to 4.4%, 0.03, −1.63, 0.01, 0.16, and 3.1%, respectively, when DEPSO is used under a 1 h time horizon. Meanwhile, these values reach 14.2%, 0.05, −3.67, 0.03, 0.19, and 9.2% for PSO and 9.4%, 0.06, −8.25, 0.064, 0.2, and 6.3% for DE under a 1 h time horizon. A comparison under different time horizons is highlighted in Table 5. Traditional methods like regression **model** and autoregressive moving average models have drawbacks of non-linear fitting capabilities which is addressed in the proposed **model**. Finally, the use of the DEPSO hybrid metaheuristic **algorithm** in **short**-**term** forecasting is supported by its simplicity, robustness, and novelty of implementation. DEPSO is also more computationally efficient

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Wind **power** plays a leading role in the development of renewable energy. However, the random nature of wind turbine **power** and its associated uncertainty create challenges in dispatching this **power** effectively in the **power** system, which can result in unnecessary curtailment of the wind turbine **power**. Improving the accuracy of wind turbine **power** forecasting is an effective measure for resolving such problems. This study uses a deep learning network to forecast the wind turbine **power** **based** on a long **short**-**term** memory network (LSTM) **algorithm** and uses the Gaussian mixture **model** (GMM) to analyze the error distribution characteristics of **short**-**term** wind turbine **power** forecasting. The LSTM **algorithm** is used to forecast the **power** and uncertainties for three wind turbines within a wind farm. According to numerical weather **prediction** (NWP) data and historical **power** data for three turbines, the forecasting accuracy of the turbine with the largest number of training samples is the best of the three. For one of the turbines, the LSTM, radial basis function (RBF), wavelet, deep belief network (DBN), back propagation neural networks (BPNN), and Elman neural network (ELMAN) have been used to forecast the wind turbine **power**. This study compares the results and demonstrates that LSTM can greatly improve the forecasting accuracy. Moreover, this study obtains different confidence intervals for the three units according to the GMM, mixture density neural network (MDN), and relevance vector machine (RVM) **model** results. The LSTM method is shown to have higher accuracy and faster convergence than the other methods. However, the GMM method has better performance and evaluation than other methods and thus has practical application value for wind turbine **power** dispatching.

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In the last decade, tens of **short**-**term** wind **power** forecasting models have been described in the international literature. Nevertheless, despite the fact that future contributions of PV plants to the global electricity consumption will be comparable to that corresponding to wind farms, **short**-**term** forecasting models for PV plants are in their early stages. Most of the published works corresponding to **short**-**term** forecasting models for PV plants are oriented to solar radiation predictions [6–9], while only a few works describe models aimed at directly forecasting the hourly **power** production in PV plants [10–17]. Most of these published models are **based** on artificial neural networks (ANNs). A hybrid approach with the combination of a data filtering technique **based** on wavelet transformation and ANNs is presented in [10] and used to obtain one-hour-ahead **power** output forecasts. Several forecasting techniques are evaluated and compared in [11] for predicting the **power** output of a PV plant with forecasting horizons of 1 and 2 h ahead; the best results are obtained with models **based** on ANNs optimized with Genetic **Algorithm** (GA). A **model** **based** on recurrent neural networks to forecast hourly insolation and temperature for the next 24 h is described in [12]; both forecasts are used to calculate the hourly **power** generation in the PV plant. Support vector machines are used in [13] and [14] to forecast directly the hourly **power** generation for the next 24 h. In [15] a multilayer perceptron ANN optimized with GAs is used to provide hourly **power** generation in a PV installation for the 24 h of the next day. In all these works describing forecasting models with horizons covering 24 h, some forecasted weather variables (such as global solar radiation, temperature, relative humidity or cloudiness, obtained from a NWP tool), are used as inputs in the forecasting **model**. Even these forecasted weather values are used in [16] to forecast the hourly **power** production for all PV plants in a local or regional scale. Genetic programming of evolution of fuzzy rules has been proposed in [17] to estimate the output of a PV plant, allowing the selection of the best forecasting **model**.

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The work aims at implementing a method to compute **prediction** intervals (PIs) for PV AC active **power**. Probabilistic forecast is important in microgrids robust control as it repre- sents a fundamental information for decision making under uncertainty, besides the more common single-point forecast [3]. The computed PIs target a sub-second time horizon from 100 ms up to 500 ms (i.e., ultra-**short**-**term**). The proposed method, called Dynamic Interval Predictor (DIP), was ﬁrstly introduced in [1] for sub-second irradiance forecast and it is here improved and extended in several aspects. First, the methodology is applied to directly forecast the PV AC active **power** by only using past AC **power** measurements and without the need of any irradiance sensing system. The direct forecast of the AC active **power** has the advantage of avoiding the introduction of an intermediate **model** of the PV system. The DIP is **based** on the experimentally veriﬁed correlation between the derivative of the PV AC active **power** and the errors caused by a generic point forecast method. Differently from [1], we compute here the absolute error instead of the relative one since it proved to be more indicative of the forecast uncertainty, as explained in subsection IV-B. Moreover, we propose an improved approach that consists in clustering the mentioned correlations as a function of the AC active **power** value itself. In other words, we consider the value of the AC active **power** as a further variable that is expected to inﬂuence the error caused by the point forecast. As a further last step beyond [1], we describe how the **algorithm** can be embedded into an industrial microcontroller and adopted within a real- time control framework. As example, we refer to the control solution introduced in [4], [5], where software agents, speciﬁc for each resource, make use of the PIs to compute device- agnostic belief functions. In this applications the agents are able to communicate among each other’s using a simple protocol with a refresh rate of around 100 ms (Section II).

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Fossil fuels are the primary source of energy worldwide, accounting for 84% of primary energy use [1]. However, the world seeks alternatives, and renewable energy has gained interest: its consumption share has grown strongly to over 40% (excluding hydroelectricity) of the global growth in primary energy in 2019 compared to 2018, with solar and wind **power** being the greatest beneficiaries [1]. The chief concern that accompanies solar energy is its uncertainty, which is mainly affected by weather conditions [2]. Being able to predict **power** generation mere hours ahead would help control the amount that must be generated from fossil fuels, reducing the amount of carbon dioxide (CO2) emissions produced. There are two main forecasting methods: **model**-**based** approaches and data- driven approaches. An example of **model**-**based** approaches is numerical weather **prediction** (NWP) which employs a set of equations to describe the flow of fluids. **Model**-**based** ap- proaches can be complicated and computationally costly. Data- driven approaches, on the other hand, do not use any physical **model**. They are easier to implement and require no prior knowledge about weather forecasts. Forecasting techniques can be deterministic or probabilistic [2] [3]. Probabilistic approaches can predict a range of values with probabilities, and thus are more appropriate for forecasting applications.

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The exponential non-linearity of current-voltage equations causes many difficulties in **prediction** and extraction of the electric, dynamic or thermal parameters [28] while, the implicit models are not capable of determining the behavior of the **photovoltaic** cell/module under many effects. Furthermore, solar cell models have multi-modal objective functions and **model** parameters vary with operational conditions such as temperature and irradiance. The main problem is to identify the optimal parameter values such as photo-generated current, diode saturation current, series resistance, and diode quality factor. Over the years, various papers have been presented and developed different techniques to identify the optimal values of the electric parameters to describe the behavior of the characteristics. These can be categorized into analytical methods, numerical methods and metaheuristic methods. There are several analytical and numerical (generally gradient- **based**) methods, as described in Table 1.

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The Western Dataset [35] created by 3TIER with oversight and assistance from NREL is used to validate the proposed WPP **model**. In the Western Dataset, NWP models were used to es- sentially recreate the historical weather for the western U.S. for the years of 2004, 2005, and 2006. The modeled data was sam- pled every 10 min temporally and every 2 km spatially. 3TIER modeled the **power** output of 10 wind turbines at 100 m above ground level on each grid point using a technique called the Sta- tistical Correction to Output from a Record Extension (SCORE) [36], which replicates the stochastic nature of the wind plant output. NREL modeled the hysteresis effect of the wind turbines to further replicate the real operation of wind plants. The data includes wind speed, rated **power**, SCORE-lite **power**, etc.

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In [17], a forecasting method was presented with a switching regime **based** on artificial intelligence to predict wind **power**, specifically the extreme events associated with the uncertainty of numerical weather **prediction** (NWP). The NN used was **based** on resonance theory and probabilistic methods, and was tested at two different wind farms, namely one in Denmark with historical data from 2000 to 2002 and one at Crete, Greece, with historical data from 2006 to 2008. In [18], the problem regarding the large penetration of new wind farms into the electric grid was tackled, reviewing the pros and cons, and the advances in wind **power** forecasting approaches.

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Our proposed **model** uses various methods of extracting properties and machine learning algorithms in order to analyze multiple rating inputs for each type of product. Not to forget to mention that every machine learning **algorithm** has its own strength and weakness which depends on initial data. As stated above, predicting long-**term** product ratings will meet different goals on both sides of the users and online store owners. On the side of the users, the main goal is to eliminate doubt of potential customers and raise the purchasing decision, while on the side of the owners there are other purposes, such as increased sales and commercial profits, increased online store rate in search engines, improved situation in SEO, increased welfare and continuous return of users, compatible user services, user **based** marketing, targeted internal social network for engaging and encouraging users to put more ratings into consideration.

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Other classical options are neural networks. For time series forecasting, recurrent neural networks such as long-**short**-**term** memory NNs and gated recurrent units NNs, which were originally designed for sequential data (e.g., speech recognition and natural language processing), have also been used for time series forecasting. However, their use for arbitrarily large forecast horizons has been quite limited. This section shows a comparison of the **Short**-**Term** Pattern Similarity (STPS) technique used here with other similar approaches that also allowed the same forecast horizon at a reasonable computational price. Concretely, αβ Water Demand Forecast (αβ-WDF) [ 31 ] and Generalized Regression Neural Network (GRNN) [ 6 ] approaches were analyzed.

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Although suicide ideation is a well-documented risk factor for suicidal behavior, the majority of those with suicidal thoughts do not go on to make a suicide plan or attempt. Therefore, it is vital to improve **prediction** of which individuals are likely to act on their suicidal thoughts. Unfortunately, most identified risk factors, such as major depression, predict suicide ideation but not attempts among those thinking about suicide. 3, 13 Data from the WHO WMH Surveys indicate that, whereas known risk factors account for 62.4% and 80.3% of the variance in predicting suicide ideation and attempts, respectively, these same risk factors account for only 7.1% of the variance predicting suicide attempts among ideators. 3 Recent research has started to identify some risk factors that do differentiate suicide attempters from suicide ideators, including younger age, low income or

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In the paper energy supply system **based** on **photovoltaic** (PV) arrays was described. Also models of a single PV cell and a voltage boost converter were described. The boost converter was used for holding an appropriate work state of the PV arrays associated with its maximum **power** point level in various work conditions associated with irradiance level and the arrays temperature. Finally, comparison of two strategies of voltage level control in PV arrays system was put forward. These strategies were used to attain the maximum **power** point, and to define the work conditions, in which described control algorithms are the most effective.

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