The omnipresent role of online information and communication technology (ICT) channels in the lives of Millennial consumers is universally recognised in industry and academia. The persistent usage of ICT platforms such as social media, especially digital video sharing conduits (e.g., YouTube), among the Millennial cohort has become an important marketing communication platform for organisations to reach this evasive target market. The extensive use of YouTube has generated billions of dollars in marketing communication income, but there is limited academic inquiry in terms of in developing economies, particularly regarding the effect of online usage and demographic factors among Millennials. This paper examines the effect of YouTube marketing communication on affective (attitudinal) responses, meaning brand liking and the impact on brand preference, among Millennials in two developing economies, Romania and South Africa, as well as the influence of usage and demographic factors on the affective (attitudinal) association. A survey was conducted among 400 Romanian and 400 South African respondents, and the hypothesised associations were evaluated via structural equation modelling (SEM) and multigroup SEM. The results of this paper reveal a favourable connection between brand liking and brand preference as a result of YouTube marketing communication, making a notable contribution to the limited YouTube inquiry on attitude-to-advertising theory in developing economies regarding brands in general and sustainable offers in particular. A number of the online usage and demographic factors were also found to have an effect on the brand liking and preference association, supporting in the reduction of the academic–practitioner gap, and assisting organisations in better understanding Millennials in the development of effective marketing communication campaigns on video sharing platforms.

This paper focuses on an important issue regarding the forecasting of the hourly energy consumption in the case of large electricity non-household consumers that account for a significant percentage of the whole electricity consumption, the accurate forecasting being a key-factor in achieving energy efficiency. In order to devise the forecasting solutions, we have developed a series of dynamic neural networks for solving nonlinear time series problems, based on the non-linear autoregressive (NAR) and non-linear autoregressive with exogenous inputs (NARX) models. In both cases, we have used large datasets comprising the hourly energy consumption recorded by the smart metering device from a commercial center type of consumer (a large hypermarket), while in the NARX case we have used supplementary temperature and time stamps datasets. Of particular interest was to research and obtain an optimal mix between the training algorithm (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient), the hidden number of neurons and the delay parameter. Using performance metrics and forecasting scenarios, we have obtained results that highlight an increased accuracy of the developed forecasting solutions. The developed hourly consumption forecasting solutions can bring significant benefits to both the consumers and electricity suppliers.

In this paper, we report a study having as a main goal the obtaining of a method that can provide an accurate forecast of the residential electricity consumption, refining it up to the appliance level, using sensor recorded data, for residential smart homes complexes that use renewable energy sources as a part of their consumed electricity, overcoming the limitations of not having available historical meteorological data and the unwillingness of the contractor to acquire such data periodically in the future accurate short-term forecasts from a specialized institute due to the implied costs. In this purpose, we have developed a mixed artificial neural network (ANN) approach using both non-linear autoregressive with exogenous input (NARX) ANNs and function fitting neural networks (FITNETs). We have used a large dataset containing detailed electricity consumption data recorded by sensors, monitoring a series of individual appliances, while in the NARX case we have also used timestamps datasets as exogenous variables. After having developed and validated the forecasting method, we have compiled it in view of incorporating it into a cloud solution, being delivered to the contractor that can provide it as a service for a monthly fee to both the operators and residential consumers.