Abstract:Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as this requires training several different models for selecting the best amongst them along with substantial feature engineering to derive informative features and finding optimal time lags, a commonly used input features for time series models. Methods: Our approach uses machine learning and a long short-term memory (LSTM)-based neural network with various configurations to construct forecasting models for short to medium term aggregate load forecasting. The research solves above mentioned problems by training several linear and non-linear machine learning algorithms and picking the best as baseline, choosing best features using wrapper and embedded feature selection methods and finally using genetic algorithm (GA) to find optimal time lags and number of layers for LSTM model predictive performance optimization. Results: Using France metropolitan's electricity consumption data as a case study, obtained results show that LSTM based model has shown high accuracy then machine learning model that is optimized with hyperparameter tuning. Using the best features, optimal lags, layers and training various LSTM configurations further improved forecasting accuracy. Conclusions: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area.

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Different parts of the world have different cultures and culture has an impact on the way people perceive, plan and execute any assignment. The success and failure stories of software projects reveal that human factors are one of the significantly important issues. Psychological theories assert that people have different personality traits and these personality traits are pigeonholed by soft skills or emotional intelligence.Most of the studies carried out on human factor in software engineering concentrate primarily on personality traits. However, soft skills which largely determine personality traits have been given comparatively little attention by researchers from software engineering community. The main objective of this work is to find out whether employers' soft skills requirements, as advertised in job postings, within different roles of software development are similar across different cultures. We used a dataset of 500 job descriptions from four different regions of the world in this study. We found that in the cases of designer, programmer and tester substantial similarity exits for the requirements of soft skills whereas only in case of system analyst dissimilarity is present across different cultures.