The energy output a photo voltaic(PV) panel is a function of solar irradiation and weather parameters like temperature and wind speed etc. A general measure for solar irradiation called Global Horizontal Irradiance (GHI), customarily reported in Watt meter @math , is a generic indicator for this intermittent energy resource. An accurate prediction of GHI is necessary for reliable grid integration of the renewable as well as for power market trading. While some machine learning techniques are well introduced along with the traditional time-series forecasting techniques, deep-learning techniques remains less explored for the task at hand. In this paper we give deep learning models suitable for sequence to sequence prediction of GHI. The deep learning models are reported for short-term forecasting @math hour along with the state-of-the art techniques like Gradient Boosted Regression Trees(GBRT) and Feed Forward Neural Networks(FFNN). We have checked that spatio-temporal features like wind direction, wind speed and GHI of neighboring location improves the prediction accuracy of the deep learning models significantly. Among the various sequence-to-sequence encoder-decoder models LSTM performed superior, handling short-comings of the state-of-the-art techniques.
Traditionally time series forecasting was under the scope of statistics and models like Auto Regression Integrated Moving Average (ARIMA), Seasonal ARIMA were extensively used linear models @cite_2 . But due to possible non-linearity in the data Artifical Neural Networks @cite_6 were introduced and subsequently hybrid ARIMA models were used @cite_20 @cite_8 . Random Forest based methods were reported used in forecasting. @cite_12 , however the above were outperformed by Gradient Boosted Regression Tree (GBRT) while used for forecasting problem in Global Energy Forecasting Competition 2014 @cite_16 . A summary of related PV power forecasting models is available in @cite_10 .
@cite_8 @cite_6 @cite_2 @cite_16 @cite_10 @cite_12 @cite_20
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Abstract Autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting during the past three decades. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. ARIMA models and ANNs are often compared with mixed conclusions in terms of the superiority in forecasting performance. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately. A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA-ANN model for the prediction of time series data. Many of the hybrid ARIMA-ANN models which exist in the literature apply an ARIMA model to given time series data, consider the error between the original and the ARIMA-predicted data as a nonlinear component, and model it using an ANN in different ways. Though these models give predictions with higher accuracy than the individual models, there is scope for further improvement in the accuracy if the nature of the given time series is taken into account before applying the models. In the work described in this paper, the nature of volatility was explored using a moving-average filter, and then an ARIMA and an ANN model were suitably applied. Using a simulated data set and experimental data sets such as sunspot data, electricity price data, and stock market data, the proposed hybrid ARIMA-ANN model was applied along with individual ARIMA and ANN models and some existing hybrid ARIMA-ANN models. The results obtained from all of these data sets show that for both one-step-ahead and multistep-ahead forecasts, the proposed hybrid model has higher prediction accuracy. Abstract Forecasting of energy demand in emerging markets is one of the most important policy tools used by the decision makers all over the world. In Turkey, most of the early studies used include various forms of econometric modeling. However, since the estimated economic and demographic parameters usually deviate from the realizations, time-series forecasting appears to give better results. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) methods to estimate the future primary energy demand of Turkey from 2005 to 2020. The ARIMA forecasting of the total primary energy demand appears to be more reliable than the summation of the individual forecasts. The results have shown that the average annual growth rates of individual energy sources and total primary energy will decrease in all cases except wood and animal–plant remains which will have negative growth rates. The decrease in the rate of energy demand may be interpreted that the energy intensity peak will be achieved in the coming decades. Another interpretation is that any decrease in energy demand will slow down the economic growth during the forecasted period. Rates of changes and reserves in the fossil fuels indicate that inter-fuel substitution should be made leading to a best mix of the country's energy system. Based on our findings we proposed some policy recommendations. Financial time series forecasting has become a challenge because it is noisy, non-stationary and chaotic. Most of the existing forecasting models for this problem do not take market sentiment into consideration. To overcome this limitation, motivated by the fact that market sentiment contains some useful forecasting information, this paper uses textual information to aid the financial time series forecasting and presents a novel text mining approach via combining ARIMA and SVR (Support Vector Regression) to forecasting. The approach contains three steps: representing textual data as feature vectors, using ARIMA to analyze the linear part and developing a SVR model based only on textual feature vector to model the nonlinear part. To verify the effectiveness of the proposed approach, quarterly ROEs (Return of Equity) of six security companies are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results. It indicates that the proposed model that uses additional market sentiment provides a promising alternative to financial time series prediction. Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package. The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted. The price data is sourced from the Bitcoin Price Index. The task is achieved with varying degrees of success through the implementation of a Bayesian optimised recurrent neural network (RNN) and a Long Short Term Memory (LSTM) network. The LSTM achieves the highest classification accuracy of 52 and a RMSE of 8 . The popular ARIMA model for time series forecasting is implemented as a comparison to the deep learning models. As expected, the non-linear deep learning methods outperform the ARIMA forecast which performs poorly. Finally, both deep learning models are benchmarked on both a GPU and a CPU with the training time on the GPU outperforming the CPU implementation by 67.7 . We investigate the probabilistic forecasting of solar and wind power generation in connection with the Global Energy Forecasting Competition 2014. We use a voted ensemble of a quantile regression forest model and a stacked random forest–gradient boosting decision tree model to predict the probability distribution. The raw probabilities thus obtained need to be post-processed using isotonic regression in order to conform to the monotonic-increase attribute of probability distributions. The results show a great performance in terms of the weighted pinball loss, with the model achieving second place on the final competition leaderboard. This paper, bases on the trend and seasonal fluctuation of China's monthly railway freight, uses ARIMA model and Holt-Winters model to forecast. After comparison of forecasting results, a final freight result from January to December of 2010 is generated. After verification, the predict results of ARIMA model and Holt-Winters model, with errors lower than 4 , are preferable to be used of railway freight forecast. This paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand profile from one day to the next, and a within-week seasonal cycle is evident when one compares the demand on the corresponding day of adjacent weeks. There is strong appeal in using a forecasting method that is able to capture both seasonalities. The multiplicative seasonal ARIMA model has been adapted for this purpose. In this paper, we adapt the Holt-Winters exponential smoothing formulation so that it can accommodate two seasonalities. We correct for residual autocorrelation using a simple autoregressive model. The forecasts produced by the new double seasonal Holt-Winters method outperform those from traditional Holt-Winters and from a well-specified multiplicative double seasonal ARIMA model. Abstract A feedforward neural network which can account for nonlinear relationships was used to compare ARIMA and neural network price forecasting performance. Data used was monthly live cattle and wheat prices from 1950 through 1990. The experiment was repeated seven times for successive three year periods. This involved using a walk forward or sliding window approach from 1970 through 1990 which generated out of sample results. The neural network models achieved a 27 percent and 56 percent lower mean squared error than ARIMA model. The absolute mean error and mean absolute percent error were also lower for the neural network models. The neural network models were able to capture a significant number of turning points for both wheat and cattle, while the ARIMA model was only able to do so for wheat. Since this forecasting method is not problem specific and uses only past prices, it can be applied to other forecasting problems such as stocks and other financial prices. Both linear and nonlinear models have been proposed for forecasting the power output of photovoltaic systems. Linear models are simple to implement but less flexible. Due to the stochastic nature of the power output of PV systems, nonlinear models tend to provide better forecast than linear models. Motivated by this, this paper suggests a fairly simple nonlinear regression model known as multivariate adaptive regression splines (MARS), as an alternative to forecasting of solar power output. The MARS model is a data-driven modeling approach without any assumption about the relationship between the power output and predictors. It maintains simplicity of the classical multiple linear regression (MLR) model while possessing the capability of handling nonlinearity. It is simpler in format than other nonlinear models such as ANN, k-nearest neighbors (KNN), classification and regression tree (CART), and support vector machine (SVM). The MARS model was applied on the daily output of a grid-connected 2.1kW PV system to provide the 1-day-ahead mean daily forecast of the power output. The comparisons with a wide variety of forecast models show that the MARS model is able to provide reliable forecast performance. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. An introduction to forecasting Basic statistical concepts Forecasting by using regression analysis Simple linear regression Multiple regression Topics in regression analysis Forecasting by using time series regression, decomposition methods and exponential smoothing Time series regression Decomposition methods Exponential smoothing Forecasting by using basic techniques of the box Jenkins methodology Nonseasonal, box-Jenkins models and their tentative identification Estimation, diagnostic checking and forecasting for nonseasonal box-Jenkins models An introduction to box-Jenkins seasonal modelling Forecasting by using advanced technology of the box-Jenkins methodology General box-Jenkins seasonal modelling Using the box-Jenkins methodology to improve time series regression models and to implement exponential smoothing Transfer functions and intervention models. In last decade, the higher penetration of renewable energy resources (RES) in energy market was encouraged by implementing the energy polices in several developed and developing countries due to increasing environmental concerns. Among wide range of RES, Photovoltaic (PV) electricity generation get higher attention by researcher, energy policy makers and power production companies due to its economic and environmental benefits. Therefore, a large PV penetration was observed in energy market with rapid growth in the last decade. The PV output power is highly uncertain due to several meteorological factors such as temperature, wind speed, cloud cover, atmospheric aerosol levels and humidity level. The inherent variability of PV output power creates different issues directly or indirectly for power grid such as power system control and reliability, reserves cost, dispatchable and ancillary generation, grid integration and power planning. Therefore, there is need to accurately forecast the PV output over the spectrum of forecast horizon at different chronological scales. In this paper, a comprehensive and systematic review of PV output power forecast models were provided. This review covers the different factors affecting PV forecast, PV output power profile and performance matrices to evaluate the forecast model. The critical analysis regressive and artificial intelligence based forecast models are also presented. In addition, the potential benefits of hybrid techniques for PV forecast models are also thoroughly discussed. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Abstract We study the behavior at infinity of the tail of the stationary solution of a multidimensional linear auto-regressive process with random coefficients. We exhibit an extended class of multiplicative coefficients satisfying a condition of irreducibility and proximality that yield to a heavy tail behavior. To cite this article: B. de , C. R. Acad. Sci. Paris, Ser. I 339 (2004). From the Publisher: This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control. Features sections on: recently developed methods for model specification, such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods. This paper proposes a method for selecting explanatory variables for global solar radiation forecasting. The weather conditions affect generation output of renewable energy significantly. As a result, smart grids increase the uncertainties caused by the power injections of photovoltaic (PV) and or wind power generation. To smooth smart grid operation, it is necessary to select the predicted input variables for the forecasting model. In this paper, how to select explanatory variables in the forecasting model of PV generation is discussed because Japan gives much higher priority to PV generation in the framework of future energy policy. The global solar radiation is one of the most important variables in dealing with PV generation output forecasting. This paper focuses on the relationship between the global solar radiation and its explanatory variables. The proposed method makes use of the CART (Classification and Regression Trees) algorithm of data mining method to select the explanatory or input variables in the forecasting model of global solar radiation. CART has the function to give priority to explanatory variables through an index called Variable Importance. The proposed method was applied to real data of global solar radiation in Tokyo, Japan. An accurate forecast of the exploitable energy from Renewable Energy Sources, provided 24 h in advance, is becoming more and more important in the context of the smart grids, both for their stability issues and the reliability of the bidding markets. This work presents a comparison of the PV output power day-ahead forecasts performed by deterministic and stochastic models aiming to find out the best performance conditions. In particular, we have compared the results of two deterministic models, based on three and five parameters electric equivalent circuit, and a hybrid method based on artificial neural network. The forecasts are evaluated against real data measured for one year in an existing PV plant located at SolarTechlab in Milan, Italy. In general, there is no significant difference between the two deterministic models, being the three-parameter approach slightly more accurate (NMAE three-parameter 8.5 vs. NMAE five-parameter 9.0 ). The artificial neural network, combined with clear sky solar radiation, generally achieves the best forecasting results (NMAE 5.6 ) and only few days of training are necessary to provide accurate forecasts.