The continuous penetration of renewable energy resources (RES) into the energy mix and the transition of the traditional electric grid towards a more intelligent, flexible and interactive system, has brought electrical load forecasting to the foreground of smart grid planning and operation. Predicting the electric load is a challenging task due to its high volatility and uncertainty, either when it refers to the distribution system or to a single household. In this paper, a novel methodology is introduced which leverages the advantages of the state-of-the-art deep learning algorithms and specifically the Convolution Neural Nets (CNN). The main feature of the proposed methodology is the exploitation of the statistical properties of each time series dataset, so as to optimize the hyper-parameters of the neural network and in addition transform the given dataset into a form that allows maximum exploitation of the CNN algorithm’s advantages. The proposed algorithm is compared with the LSTM (Long Short Term Memory) technique which is the state of the art solution for electric load forecasting. The evaluation of the algorithms was conducted by employing three open-source, publicly available datasets. The experimental results show strong evidence of the effectiveness of the proposed methodology.

The present study attempted to translate and culturally adapt an established research instrument, the Hypoglycemia Fear Survey (HFS) questionnaire, to the Greek population and evaluate its validity and internal consistency so that it can be used for the assessment of hypoglycemia fear in Greek children and adolescents with T1DM and their parents. One hundred Greek children and adolescents with T1DM, 54 males, 6–18 years old, and one of their parents participated in this validation study. The participants completed the translated Greek HFS, which includes one version for children (CHFS) and one for parents (PHFS). Exploratory Factor Analysis (EFA) was used to assess construct validity. Internal consistency was assessed using Cronbach’s alpha, and convergent validity was established by estimating the correlation coefficients between the scores of the HFS scales/subscales and the different constructs of the Pediatric Quality of Life Inventory. The CHFS and PHFS exhibited adequate internal consistency for the total score and the Worry subscale, but lower consistency for the Behavior subscale. High test–retest reliability was also shown. We conclude that the Greek version of the HFS is a valid and reliable instrument to assess the fear of hypoglycemia in Greek children and adolescents with T1DM and their parents.

Although self-rating mania scales have been developed, a lack of such instruments validated for the Greek population is noted. This study aims to examine the validity, reliability and psychometric properties of the Altman Self Rating Mania Scale (ASRM) adapted in Greek (G-ASRM). A sample of 86 consecutive inpatient and outpatient bipolar patients diagnosed by the DSM-5 criteria and 37 healthy controls were assessed by using the Young Mania Rating Scale (YMRS) and the Montgomery Asberg Depression Rating Scale (MADRS), and self-administered the G-ASRM. Factor analysis, test-retest analysis, measurement invariance tests, mean differences, Pearson's Correlation analysis and ROC analysis were used to confirm the validity of G-ASRM as a scale, test its reliability, study its psychometric properties in different subgroups and establish a cut-off value for indicating the presence of (hypo)mania in BD patients. Also, regression models were built to expose dependencies between YMRS and G-ASRM items. Monofactoriality of the scale was verified, based on Exploratory Factor Analysis (EFA). Cronbach's alpha was 0.895. G-ASRM is highly correlated with YMRS (r = 0.856, p < 0.0005) and uncorrelated with MADRS (r = -0.051, p = 0.623). Test- retest r-coefficient was calculated at 0.85. The optimal cut-off score, set at ≥6 for (hypo)mania assessment, is in agreement with the results reported for the original version. Limitations of the study are that the scale was not normed on diagnostic groups other than bipolar, nor was it administered longitudinally, so as to assess its sensitivity to symptom changes overtime. The G-ASRM can be validly and reliably used in the Greek population for the assessment of (hypo)mania in bipolar patients.