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Studies on the relationship between brand equity and long-term promotional effectiveness have many issues. Brand equity measures can be classified as either consumer-based (CB) or firm-based (FB) measures. Previous literature has mostly focused on one or the other type of measure, mostly due to computational difficulties or data limitations, making it difficult to determine whether the results can be generalized to all types of measure. In addition, long-term promotional effectiveness remains a debatable issue due to complicated long-term effects. Our study is the first to include both CB and FB brand equity measures in a long-term promotional effectiveness study. Using weekly scanner data in the breakfast category covering a three-year period, we are able to compute a variety of popular operationalizations of brand equity measures by employing the aggregate demand model and capture total long-term effects via the persistence model. Our major finding is that the general consensus that higher brand equity generates higher promotional effectiveness applies only to typical FB measures—not CB measures. Moreover, we show a positive accumulated long-term effectiveness and an absence of permanent effects for all our brands. These results offer new insights into the differentiation of major brand equity measures and long-term promotional effectiveness. [ABSTRACT FROM AUTHOR]

This research aims to predict year-ahead ambient temperature aggregated at monthly level per hour on airport locations using historical temperature data. In this research, an Extreme gradient boosting regression model, LSTM model and the benchmark model, a persistence model, are compared in accuracy. Models are fine-tuned on the cities with the highest variability in temperature and grid searches are implemented only for these cities (one per data source). Overall, we have seen that the LSTM model with output size 12 months x 24 hours predicts for the next year the best. The Persistence model is closely followed by the Extreme gradient boosting model, with a small deviation in the quantiles. The point predictions for each of the other models are a bit further of (with more than 3 degrees Celsius) and LSTM 365 days x 24 hours is the worst in this case. These models can be used to give an indication for the ambient temperature on lane level. [ABSTRACT FROM AUTHOR]

A Gaussian process regression (GPR) with active learning is proposed for developing the solar irradiance point and interval forecasting models, which consider the spatial‐temporal information collected from a targeted site and a number of neighbouring sites. To enhance the performance of the GPR‐based model an active learning process is developed for constructing an ad‐hoc input feature set, selecting training data points, and optimising hyper‐parameters of GPR models. To validate the advantages of the proposed method, a comprehensive computational study is conducted based on solar irradiance data collected from the northwest California area. In the point forecasting, the proposed method beats the state‐of‐the‐art benchmarking methods including classical statistical models and data‐driven models according to values of the normalised root mean squared error, normalised mean absolute error, normalised mean bias error, and coefficient of determination. In the interval forecasting, the proposed method outperforms the persistence model, autoregressive model with exogenous inputs, generic GPR, as well as two recently reported forecasting methods, the bootstrap‐based extreme learning machine and quantile regression, in terms of the forecasting reliability. Computational results show that the proposed method is more effective than well‐known existing benchmarks in the point and interval forecasting of the solar irradiance. [ABSTRACT FROM AUTHOR]

Abstract With increased integration of wind energy systems, an accurate wind speed forecasting technique is a must for the reliable and secure operation of the power network. Statistical methods such as Auto-Regressive Integrated Moving Average (ARIMA) and hybrid methods such as Wavelet Transform (WT) based ARIMA (WT-ARIMA) model have been the popular techniques in recent times for short-term and very short-term forecasting of wind speed. However, the contribution of the forecasting error due to different decomposed time series on the resultant wind speed forecasting error has yet not been analyzed. This paper, thus explores this shortcoming of the ARIMA and WT-ARIMA models in forecasting of wind speed and proposes a new Repeated WT based ARIMA (RWT-ARIMA) model, which has improved accuracy for very short-term wind speed forecasting. A comparison of the proposed RWT-ARIMA model with the benchmark persistence model for very short-term wind speed forecasting, ARIMA model and WT-ARIMA model has been done for various time-scales of forecasting such as 1 m i n , 3 m i n , 5 m i n , 7 m i n and 10 m i n. This comparison proves the superiority of the proposed RWT-ARIMA model over other models in very short-term wind speed forecasting. Highlights • A new Repeated WT based ARIMA (RWT-ARIMA) model is proposed for very short-term wind speed forecasting. • Wavelet decomposition is used to extract wind speed in different frequency bands. • The proposed method is capable of comprehensively capturing gust and turbulence in wind speed. • The proposed model is tested for different time-scales of forecasting and its superiority over benchmark models is verified. [ABSTRACT FROM AUTHOR]

This study proposes a novel prediction model for uni‐ and multi‐variable forecast, where error feedback is added to the original forecast value predicted using the persistence model. The error feedback mechanism is constructed to find out the relationship between the errors and the original forecast values. Simulation studies are carried out using wind power data obtained from two databases, and the results demonstrate that the proposed model provides a more accurate and stable forecast compared to other methods. Based on this, the economic benefit of accurate wind power forecast has been analysed for power system dispatch, which aims to minimise the operation cost. The dispatch results of two scenarios have shown that accurate forecast result decreases the cost of reserve capacity, balancer set invoking capacity and the possibility of wind curtailment, which leads to more economic dispatch of power systems. [ABSTRACT FROM AUTHOR]

As a characteristic in bacterial colony, persistence model described the dynamics of two subpopulations (normal ( n ) and persister ( p )). In order to illustrate the switch of “Threshold” in the stepwise behavior responses of organisms, it is hypothesized that total behavior ( B t ) of organisms consists of two types in behavior tendency, intoxication ( B p ) and normal/recovery behavior ( B n ). Both B p and B n could be concurrently affected by environmental stress E , and behavior response modes ( M ) are decided by the relationship between E and toxicity threshold of test organisms ( T i ). The results suggested stress constant λ was decided by the constant rates g nE , g pE , a n and a p . Due to different stress constant λ , the behavior responses of indicators showed great difference in different M , which included ‘safe mode’ ( M s ), ‘acclimation mode’ ( M ac ), ‘adjustment mode’ ( M aj ) and ‘toxic effect’ ( M te ). Usually, B t during M s could maintain around 0.8, and M te would happen once it is lower than 0.2. According to the relationship between B t values and E changes in 7 M aj s, behavior persistence relying on adjustment could reflect the behavior homeostasis of organisms under environmental stress and be regarded as a threshold switch for the stepwise behavior responses. The mathematical analysis of behavior persistence allows making a quantitative prediction on environment assessment that would promote the emergence of persistence, as well as evaluating its ecological implications. [ABSTRACT FROM AUTHOR]

Particulate matter is an air pollutant that has resulted in tremendous health effects to the exposed populace. Air quality forecasting is an established process where air pollutants particularly, particulate matter (PM) concentration is predicted in advance, so that adequate measures are implemented to reduce the health effect of PM to the barest level. The present study used daily average PM concentration and meteorological parameters (temperature, humidity, wind speed and wind direction) for 5 years (2006-2010) from three industrial air quality monitoring stations in Malaysia (Balok Baru, Tasek and Paka). Time series plot was used to assess PM pollution trend in the industrial areas. Additionally, step wise regression (SWR) analysis was used to predict next day PM concentrations for the three industrial areas. The SWR method was compared with a persistence model to assess its predictive capabilities. The results for the trend analysis showed that, Balok Baru (BB) had higher PM concentration levels, having high values in 2006, 2007 and 2009. These values were higher than the Malaysian Ambient Air Quality Guideline (MAAQG) of 150 μg/m. Subsequently, the other two industrial areas Tasek (TK) and Paka (PK) had no record of violating the MAAQG. The results for the SWR analysis had significant R values of 0.64, 0.66 and 0.60, respectively. The model performance results for variance inflation factor (VIF) were less than 5 and Durbin-Watson test (DW) had value of 2 for each of the study areas, which were significant. The comparative analysis between SWR and persistence model showed that the SWR had better capabilities, having lower errors for the BB, TK and PK areas. Using root mean square error (RMSE), the results showed error differences of 7, 12 and 16 %, and higher predictability using index of agreement (IA), having a difference of 17, 19 and 16 % for BB, TK, and PK areas, respectively. The results showed that SWR can be used in predicting PM next day average concentration, while the extreme event detection results showed that 100 μg/m were better detected than the 150 μg/m bench marked levels. [ABSTRACT FROM AUTHOR]

Abstract: The prediction of wind speed is one of the most important aspects when dealing with renewable energy. In this paper we show a new nonparametric model, based on semi-Markov chains, to predict wind speed and the energy produced by a commercial blade. Particularly, we use an indexed semi-Markov model, that reproduces accurately the statistical behavior of wind speed. The model is used to forecast future wind speed and the energy produced through a 10 kW Aircon wind turbine. We forecast one step ahead and for different time scales. In order to check the main features of the model we show, as indicator of goodness, the root mean square error between real data and predicted ones. We compare our forecasting results with those of a persistence model and of an autoregressive model. [Copyright &y& Elsevier]

• PV power intra-hour forecasting is dealt with. • A novel method is proposed based on fractional calculus, more specifically the Caputo derivative has been used. • The use of Caputo derivative allows considering information on the trend of the forecasting variable. • The prediction tool allows to provide a closed form for the predictor which is particularly suitable for online applications. • A sensitivity analysis shows that the inclusion of information on the trend of the forecasting variable in recent past could lead to accurate results. Intra-hour photovoltaic power forecasting provides essential information for real time optimal control of microgrids. At this purpose, a critical issue is the selection of the forecasting method. The choice of a forecasting method depends on many factors such as the availability of historical data, the time horizon, the lag period, and the time available for the forecast. Persistence based methods are particularly tailored for real time forecasting which require fast information and are typically a good trade-off choice when dealing with real time operation of microgrids. Their accuracy, however, could be not satisfactory in some cases such as when it appears critical to consider the trend of the power output in the last few minutes rather than only the last measured value. Derivatives help reach this goal, but fractional derivatives seem to be a more accurate choice in order to take into account the history of the variable to be forecasted as they are a promising tool for describing memory phenomena. In this paper, a novel intra-hour forecasting method is proposed based on the Caputo derivative. Numerical applications are carried out to show the efficacy of the proposed approach. Also, the accuracy of the proposed approach is tested through comparison with three models namely, persistence, derivative-persistence and auto regressive moving average models. The strength of the proposed forecasting tool is strictly related to its low computational burden without compromising accuracy. This makes of it an interesting means for real time grid operation strategies and can be of interest for the grid operators especially in vision of the changes distribution grids are witnessing with the transition to the smart grid paradigm. [ABSTRACT FROM AUTHOR]