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Wind power serves as a clean and sustainable form of energy. However, its generation is fraught with variability and uncertainty, owing to the stochastic and dynamic characteristics of wind. Accurate forecasting of wind power is indispensable for the efficient planning, operation, and grid integration of wind energy systems. In this paper, we introduce a novel forecasting method termed Temporal Collaborative Attention (TCOAT). This data-driven approach is designed to capture both temporal and spatial dependencies in wind power generation data, as well as discern long-term and short-term patterns. Utilizing attention mechanisms, TCOAT dynamically adjusts the weights of each input variable and time step based on their contextual relevance for forecasting. Furthermore, the method employs collaborative attention units to assimilate directional and global information from the input data. It also explicitly models the interactions and correlations among different variables or time steps through the use of self-attention and cross-attention mechanisms. To integrate long-term and short-term information effectively, TCOAT incorporates a temporal fusion layer that employs concatenation and mapping operations, along with hierarchical feature extraction and aggregation. We validate the efficacy of TCOAT through extensive experiments on a real-world wind power generation dataset from Greece and compare its performance against twenty-two state-of-the-art methods. Experimental results demonstrate that TCOAT outperforms existing methods in terms of both accuracy and robustness in wind power forecasting. Moreover, we conduct a generality study on an additional real-world dataset from a different climate condition and wind power characteristics. The results show that TCOAT can achieve comparable or better performance than the state-of-the-art methods, confirming the generalization ability of TCOAT. • Novel deep learning model for wind power forecasting • Temporal collaborative attention and fusion for wind data • Superior performance on real-world wind power dataset • Attention maps for interpretability and end-to-end training [ABSTRACT FROM AUTHOR]

• An integrated LCA + DEA methodology is applied to evaluate different end-of-life treatment alternatives for wind turbine decommissioning. • Circular economy-driven policy-making scenarios are suggested to enable improvements towards a more sustainable waste management. • Waste management of wind turbine blade composites is the most significant contributor to GHG emissions and non-renewable energy consumption. • The efficiency of thermal recycling processes is a key element for the transition to a circular economy framework. • Remanufacturing, repurposing or waste prevention of wind turbine blades via design-for-recycling is crucial for sustainable waste management. • Financial incentives could offset the price handicap of chemical recycling relative to cost-efficient landfill disposal practices. The purpose of this paper is to present an integrated joint application of Life Cycle Analysis (LCA) and Data Envelopment Analysis (DEA) in order to evaluate the efficiency of different end-of-life treatment options for wind turbine decommissioning, considering, technological, economic, and environmental aspects in a circular economy context. Eleven scenarios have been configurated concerning the material waste management of a representative type of wind turbine operating in Greece. Mechanical recycling, landfill disposal and advanced thermal recycling technologies, such as conventional or microwave pyrolysis are addressed. The proposed approach does not only evaluate the efficiency of each one of the different end-of-life treatment processes relative to one another, but it also suggests circular economy-driven policy-making scenarios towards more sustainable waste management in the country. Real-world data calculations indicate that improving the performance of the energy-intensive thermal recycling process could maximize the environmental benefits. Α circular zero-waste approach based on remanufacturing, repurposing or waste prevention through design-for-recycling of wind turbine blades, could also favor long-term sustainability. [ABSTRACT FROM AUTHOR]

Abstract: This paper establishes a methodology for the estimation of the primary energy savings of an industrial (sub)-sector and the primary energy savings of the total energy system due to CHP. A primary energy savings indicator within a (sub)-sector and a total primary energy savings indicator are developed which are related with the actual energy use of a (sub)-sector and the way of disposal of the excess CHP energy produced. The methodology is applied in an industrial sub-sector in Greece according to the ‘power match’ and the ‘thermal match’ CHP sizing scenarios; subsequently results are presented and the developed indicators are fully explained. It was found that the primary energy savings indicator of a sub-sector is determined by the efficiencies of the relevant technologies, and the interrelation of the ‘power to heat’ ratio of the CHP technology used and the ‘power to heat’ ratio of the sub-sector examined; the total primary energy savings indicator is determined by the efficiencies of the relevant technologies and the percentage of the CHP energy exported from the sub-sector. The methodology can be utilized for optimum CHP planning. [Copyright &y& Elsevier]

Abstract: This paper deals with the causal relationship analysis between Gross Domestic Product, Energy Intensity and CO2 emissions in Greece from 1977 to 2007, by means of Johansen cointegration tests and Granger-causality tests based on a multivariate Vector Error Correction Modeling. Results indicate that there is a set of uni-directional and bi-directional causalities among the selected time series. We performed a model Variance Decomposition Analysis using Choleski technique and we provided a comparison with other studies. The findings of the study have significant policy implications for countries like Greece as the decoupling of CO2 emissions and economic growth seems quite unlikely. [Copyright &y& Elsevier]

Abstract: During the last decade, steel constructions with glazed façades became popular for commercial buildings in Greece. Moreover, expensive metal, natural stone, marble, ceramic, granite as well as special glass is employed for aesthetic and energy efficiency reasons. This creates opportunities for the introduction of Photovoltaic (PV) modules in double façades. PV modules on south-facing building walls are better placed at a distance from the wall to allow heat rejection and avoid overheating and efficiency loss. Exploiting the rejected heat of the PV modules is also a challenge. In this paper, we examine an improved concept of incorporating PV modules to the south façades of an office building, exploiting both the electricity produced and the heat rejected by the module, to increase building energy efficiency. The PV modules are integrated to the building wall by means of a double façade, which employs intervening ducts for ventilation purposes. The ducts are heating outdoor air, which is employed to cover the ventilation needs of the building, as well as a part of the heating loads. Simulations for typical winter and summer weather and solar insolation conditions are carried out to investigate the building’s energy performance improvements. [Copyright &y& Elsevier]

Abstract: This paper describes the stochastic prediction of the hourly profile of the intensity of the global solar radiation, I(h; nj ) for any day nj at a site. The prediction model requires one, two, or three morning measurements of the global solar radiation in a day nj , makes use of a rich data bank of past years recorded data, and provides I(h; nj ) values for the rest hours of the day. The model is validated by comparing the I(h; nj ) profiles generated for Patra, Greece, with the solar radiation measurements recorded for Winter, Autumn and Spring days, when solar radiation fluctuations often appear to be strong, while also comparing with the predicted by the METEONORM package I(h; nj ) profile. Conclusions are deducted for the predictive power of the model. The proposed model, which is developed in MATLAB for the purpose of this research, provides I(h; nj ) profile predictions very close to the measured values and offers itself as a promising tool for a predictive on-line daily load management. [ABSTRACT FROM AUTHOR]

Abstract: The current paper presents an investigation of the wind power potential of Koronos village, a remote location in the northeastern part of Naxos Island, Greece, using real wind data by a measurement mast. The obtained wind characteristics were statistically analysed using the Weibull and Rayleigh distribution functions. The results from this investigation showed that the selected site falls under Class 7 of the international system of wind classification as the mean annual wind speed recorded in the area was 7.4m/s and the corresponding annual mean power density was estimated to be 420W/m2. Furthermore, the prevailing wind directions characterising the area were the northeastern and the north–northeastern. From the statistical analysis of these results, it was revealed that the Weibull model fitted the actual data better. This remark was further enhanced by the evaluation of the performance of these two distributions. [Copyright &y& Elsevier]

Abstract: This paper deals with the economic analysis and evaluation of various energy saving measures in the building sector, focusing on a domestic detached house in Greece, i.e. in a typical Mediterranean climate. In order to detect the energy saving measures that, in addition to energy benefits, can also provide economic profits, the study examines the following measures: all kinds of insulation; upgrading of the heating system; use of thermal solar systems; upgrading of lighting; upgrading of electric appliances; upgrading of the cooling system. The economic evaluation methods used for ranking the energy saving measures are the Net Present Value, the Internal Rate of Return, the Savings to Investment Ratio and the Depreciated Payback Period. It has been found that amongst the most effective energy saving methods are the upgrading of lighting, the insulation of the roof of the building and the installation of an automatic temperature control system. [Copyright &y& Elsevier]

Abstract: In recent years, energy systems have been undergoing a development trend characterised by privatisation of the most important energy sectors (electricity and natural gas) that has turned former monopolies into free-market competitors. Furthermore, community awareness of environmental impact caused by large conventional power plants is growing, together with a greater interest in distributed-generation (DG) technologies based upon renewable energy sources (RES) and cogeneration. In this context, renewable energy technologies are emerging as potentially strong competitors for more widespread use. Despite the remarkable progress attained over the past decades, RES have not yet been fully integrated into the power sector. Some RES-technologies have already achieved a significant market share. The industry is now quite mature, although far from having developed its global potential. This paper deals with the current status of DG in Greece and the presentation of a 60kWp PV power station, developed under Law 3468/06 “Production of Electricity from Renewable Energy Sources, High Efficiency Cogeneration of Heat and Power and Other Deνices”. This application is the first DG installation with fixed PV modules implemented in the country after the approval of Law 3468/06. Cash flow economic analysis of the developed DG installation is performed and the experiences related to the potential of DG in Greek electricity market is presented and discussed. [Copyright &y& Elsevier]

• The system aims to minimize the gas consumption of domestic boilers for heating. • Neural Networks and a Genetic Algorithm are used for the optimal boiler operation. • Target is to find the optimal control instructions to be sent to the boiler. • The model leads mostly to lower boiler modulation, namely lower water temperatures. • Significant gas consumption reduction attained with the operation of the system. This paper presents a novel approach that aims to minimize the natural gas consumption of domestic boilers for heating purposes, while not compromising the user's heating needs. The system utilizes gas consumption data, the conditions both inside and outside the house collected via interconnected commercial thermostats, and the heating needs of the users. The architecture of the presented system is divided in two main coordinated processes: (a) the first one consists of two Neural Networks with Convolutional and Long Short-Term Memory layers, used to predict the indoor temperature and the boiler's modulation (load percentage), whereas (b) the second process includes a Genetic Algorithm used to determine the optimal operation conditions of the boiler, by finding the boiler control instructions that meet the user's heating preferences concerning the target indoor temperature, while minimizing the total gas consumption. One main advantage of the solution is its ability to consider boilers as a black box, since it does not need to be aware of the internal mechanics. In this way, the proposed methodology can be applied to a wide range of domestic gas boilers with minimum adjustments. The overall methodology is applied to four domestic boilers in Greece spanning three cities, to capture different climatic conditions and evaluate the system performance in varying outdoor conditions. The attained results indicate that the proposed system can lead to significant gas consumption reduction through autonomously created optimal control instructions provided to the boiler. [ABSTRACT FROM AUTHOR]

This editorial is prepared for the Applied Energy Journal as a Virtual Special Issue (VSI) and it overviews the research work presented at the NAXOS 2018 6th International Conference on Sustainable Solid Waste Management, held from 13th to 16th June 2018 in Naxos Island, Greece. The research articles presented in VSI highlight the recent developments in waste valorisation for the recovery of energy, fuels and value-added products. They also cover the primary hurdles and potential solutions moving towards more sustainable society. This editorial not only presents the overall summary of the extended research papers from NAXOS 2018, but also provides an overview of the current trends and developments in the fields of waste management, waste valorization, and energy production systems. The articles published in this VSI cover a wide range of topics, including energy recovery from waste, waste to energy technologies, sustainable energy systems, anaerobic digestion, thermal arc plasma gasification, microalgal-based biorefinery, waste management, modelling of advanced gasification systems, waste valorization, and microbial fuel cell technology. 10 manuscripts, out of total 21 extended mansucripts invited, were accepted for publication in the Applied Energy Journal through peer review process conducted by the expert reviewers in the relevant fields with the aid of the guest editors. [ABSTRACT FROM AUTHOR]