This study is focused on energy production in Albania which involves different types of infrastructure at the various points of the energy production and distribution chain, as well as monitoring and early warning systems. At a time of rapid climate change, estimating the appropriate dimensions and design of such infrastructure and systems becomes crucial. The main objective is to analyze the seasonality pattern and main external climacteric factors, such as precipitation, average temperature, and water inflow. This work deals with the seasonality patterns of climacteric factors affecting energy production and considers different statistical learning methods for prediction.

Survival analysis is the analysis of time-to-event data. Two important functions in the analysis of survival data are the survival function and the hazard function. The Kaplan-Meier method is widely used to estimate the survival function. One of the objectives of the analysis of survival data might be to examine whether survival times are related to other features. A popular regression model for the analysis of survival data is the Cox proportional hazard regression model. The most commonly used approaches, for the baseline survival function, are the Breslow and Kalbfleisch-Prentice methods. These methods provide a step function estimate of the survivor function and in many instances a continuous estimate would be preferable. For these reason, in this paper we proposed a kernel smoothing technique for baseline estimator, based on Kalbfleisch-Prentice method. We start with kernel smoothing of baseline hazard function, based on Kalbfleisch-Prentice estimator and epanechnikov kernel, than we use it to calculate the baseline survival function. To evaluate the usefulness of the kernel estimator of the baseline function, in the case of right censoring, based on Kalbfleisch-Prentice estimator we conduct simulation studies across a range of conditions, by varying the sample size and censoring rate. We compare it with the smoothing of the Breslow estimator regarding bias.

The objective of this study is to analyze and compare classical time series and deep learning models for energy load prediction. Energy predictions are important for management and sustainable systems. After analyzing the climacteric factors impact on energy load (a case study in Albania) we considered classical and deep learning models to perform forecasts. We have used hourly and daily time series for a period of three years. In total respectively 26,280 hours and 1095 days. Average temperature is considered as external variable in both statistical and deep learning models. The dynamic evolution of hourly (daily) load is correlated with hourly (daily) average temperature. The performance of the proposed models is analyzed and evaluated based on accuracy measurements (MSE, RMSE, MAPE, AIC, BIC etc.) and graphics results of statistical tests. In-sample and out-of-sample accuracy is evaluated. The models show competitive performance to some recent works in the field of short-and medium-term energy load forecasts. This work may be used by stakeholders to optimize their activities and obtain accurate forecasts of energy system behavior.

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Job tenure is an important factor for both employees and employers. Sometimes, for employees, this time is a criterion in starting new job, and also for employers when they have to hire new employees. It measures the length of time that employees have been in their current job or with their current employer. Analyzing factors, which affect the job tenure, is important for companies, as well as for employee. Job tenure is a duration concepts, so we have applied survival analysis to model the tenure for Albanian employees, in several different private and public companies. First, Kaplan-Meier method is used to estimate the survival function for time. Then Cox proportional hazards model is used to assess the impact of the predictors in job termination. This study demonstrated the important role that the current age of the employee, the age at which he started the job, salary, gender, position, education, marital status and years of work in front of the current position may have on job tenure.