In recent years, a lot of progress has been reported in the field of energy disaggregation, also referred to as Non-Intrusive Load Monitoring (NILM). Despite the fact that there are many studies focusing on the residential sector, there is considerably less research interest for the industrial sector. In this paper, we present a deep neural network based on Transformers, targeted towards capturing complex patterns in long sequences of data. The proposed transformer architecture employs 1D spatial convolutions in self-attention, and modifications inside the attention computations manage to reduce computational complexity without any loss in predictive accuracy. In order to evaluate the performance of the proposed deep learning architecture, a set of experiments has been conducted using a publicly available dataset. The experimental results indicate that the proposed model achieves better disaggregation accuracy compared to other state-of-the-art NILM models.

Sleep stage classification is one of the most critical steps in the effective diagnosis and treatment of sleeprelated disorders. Classic approaches involve trained human sleep scorers, utilizing a manual scoring technique, according to certain standards. This paper examines the implementation of an algorithm for the automation of the sleep scoring process. EEG recordings data are acquired from three different groups comprising of healthy subjects and people with minor sleep disorders. A mixture of time domain and frequency domain features are extracted. Temporal feature changes are utilized in order to capture contextual information of the EEG signal. Multiple classifiers are tested, culminating in a voting classifier, achieving a maximum accuracy of 90.8% for the healthy subjects' group. The main novelty introduced by the proposed solution is the algorithm's high accuracy when tested on a mixed dataset of healthy and patient subjects. The promising capabilities that derive from the successful implementation of this solution are discussed in the conclusions.

Accurately forecasting power generation in photovoltaic (PV) installations is a challenging task, due to the volatile and highly intermittent nature of solar-based renewable energy sources. In recent years, several PV power generation forecasting models have been proposed in the relevant literature. However, there is no consensus regarding which models perform better in which cases. Moreover, literature lacks of works presenting detailed experimental evaluations of different types of models on the same data and forecasting conditions. This paper attempts to fill in this gap by presenting a comprehensive benchmarking framework for several analytical, data-based and hybrid models for multi-step short-term PV power generation forecasting. All models were evaluated on the same real PV power generation data, gathered from the realisation of a small scale pilot site in Thessaloniki, Greece. The models predicted PV power generation on multiple horizons, namely for 15 min, 30 min, 60 min, 120 min and 180 min ahead of time. Based on the analysis of the experimental results we identify the cases, in which specific models (or types of models) perform better compared to others, and explain the rationale behind those model performances.

In 2020, residential sector loads reached 25% of the overall electrical consumption in Europe and it is foreseen to stabilise at 29% by 2050. However, this relatively small increase demands, among others, changes in the energy consuming behaviour of households. To achieve this, Demand Response (DR) has been identified as a promising tool for unlocking the hidden flexibility potential of residential consumption. In this work, a holistic incentive-based DR framework aiming towards load shifting is proposed for residential applications. The proposed framework is characterised by several innovative features, mainly the formulation of the optimisation problem, which models user satisfaction and the economic operation of a distributed household portfolio, the customised load forecasting algorithm, which employs an adjusted Gradient Boosting Tree methodology with enhanced feature extraction and, finally, a disaggregation tool, which considers electrical features and time of use information. The DR framework is first validated through simulation to assess the business potential and is then deployed experimentally in real houses in Northern Greece. Results demonstrate that a mean 1.48% relative profit can be achieved via only load shifting of a maximum of three residential appliances, while the experimental application proves the effectiveness of the proposed algorithms in successfully managing the load curves of real houses with several residents. Correlations between market prices and the success of incentive-based load shifting DR programs show how wholesale pricing should be adjusted to ensure the viability of such DR schemes.

Non-intrusive load monitoring (NILM), or else energy disaggregation, aims at decomposing the aggregated energy consumption data, estimating the contribution of each appliance to the total building energy consumption. The current paper introduces a supervised algorithm for the accurate estimation of both power consumption and operational state of the appliances. A hybrid feature set consisting of electrical, temporal, and statistical features is utilized, focusing on steady state information, due to the low sampling rate (1 minute). The contribution of each feature category is experimentally validated. A predictive model is built for each appliance, utilizing Light Gradient Boosting Machine (LGBM) regression algorithm, which is a tree-based technique with accelerated training time and low computational cost. The results obtained, exploiting both private and public datasets, indicate that the proposed algorithm is able to disaggregate most of the appliances with high accuracy. The average f-score across all the disaggregated appliances is calculated at 0.77 and 0.86 for the private and the public dataset, respectively. The proposed models have also been benchmarked against other top state-of-the-art approaches, achieving high performance with high rank.