Energies

As the importance of energy efficiency and smart readiness in the building sector has been on the rise, the financial evaluation of smart-ready technologies (SRTs) remains a gap in this field. This study introduces a methodology that comparatively evaluates the cost–benefit relationship between 11 different SRTs across three European countries—Cyprus, Italy and The Netherlands. Key performance indicators (KPIs) for energy-focused aspects such as Country-Specific Energy Savings Potential (CSESP) and Seasonal Smart Efficiency Coefficient (SSEC) and financial aspects such as Smart Readiness Cost Index (SRCI), Labor Cost Impact Factor (LCIF), Return on Smart Investment (RoSI), and Smart Investment Break-Even Period (SIBEP) were used to quantify the performance of the SRTs. The results indicate that regional labor rates, energy pricing, and climatic conditions—as well as relative technology cost–benefit tradeoffs—play a significant role in the economic viability of smart-ready devices. Having low labor costs and energy pricing, Cyprus exhibited the most cost-effective outcomes among the three countries. Italy showed strong returns although the initial investments were higher. The Netherlands was observed to benefit the most from heating-oriented technologies. The study comes to the conclusion that regionally specific methods are necessary for the adoption of SRTs and that techno-economic performance cannot be assessed separately from local market dynamics. The proposed framework supports stakeholders and policymakers in smart building investment and planning by offering a scalable method for device-level benchmarking. These indicators are developed specifically for this study and are not part of the official EU SRI (Smart Readiness Indicator) methodology. Their inclusion supports device-level evaluation and complements ongoing efforts toward SRI standardization. This research directly addresses Sustainable Development Goal (SDG) 7 on Affordable and Clean Energy, as well as SDG 11 on Sustainable Development, by evaluating how smart-ready technologies can contribute to energy efficiency and decarbonization in buildings. Based on the results, further research is needed to expand the indicator framework to additional technologies, include building typology effects, and integrate dynamic factors such as CO₂ pricing and real-time tariffs.

The echelon utilization of electric vehicle batteries is regarded as an effective method for treating waste batteries, enabling the recycling and reuse of retired electric vehicle batteries. However, the efficiency of battery disassembly is a crucial factor that impacts the potential for battery recycling. When manufacturers take disassembly efficiency into account during the design phase of new electric vehicle batteries, they can significantly reduce disassembly costs at the time of decommissioning. This, in turn, incentivizes recycling and echelon utilization of waste batteries. Our research aims to promote the echelon use of waste batteries and analyze how market competition intensity and profits from battery echelon utilization influence decision-making within the battery recycling supply chain. This paper explores the effect of market competition on battery recycling and echelon utilization, while developing a supply chain model that includes a battery manufacturer responsible for determining the level of battery disassembly design and recycling waste batteries from the market, as well as a new energy vehicle manufacturer that focuses solely on recycling waste batteries. The findings indicate that as market competition increases, the battery manufacturer tends to lower both the level of battery disassembly design and the recycling price for waste batteries. Additionally, the recycling price for waste batteries offered by new energy vehicle manufacturers is also influenced by the intensity of market competition. In scenarios with low competition intensity, the recycling price tends to rise as competition intensifies. Conversely, in highly competitive markets, the recycling price decreases with increased competition. Furthermore, the overall volume of battery recycling is impacted by the intensity of market competition; in highly competitive markets, waste battery recycling is hindered. To enhance the echelon utilization of battery recycling, relevant government agencies should strive to maintain market competition at lower levels while also encouraging the recycling of batteries that do not meet usage standards. This dual approach will improve the benefits associated with the echelon utilization of waste batteries, thereby fostering greater enthusiasm for recycling among the involved enterprises.

The fast-increasing penetration of photovoltaic (PV) power raises the issue of grid stability due to its intermittency and lack of inertia in power systems. Solar irradiance forecasting effectively supports advanced control, mitigates power intermittency, and improves grid resilience. Irradiance forecasting based on data-driven methods aims to predict the direction and level of power variation and indicate quick action. This article presents a comprehensive review and comparative analysis of data-driven approaches for time-series solar irradiance forecasting. It systematically evaluates nineteen representative models spanning from traditional statistical methods to state-of-the-art deep learning architectures across multiple performance dimensions that are critical for practical deployment. The analysis aims to provide actionable insights for researchers and practitioners when selecting and implementing suitable forecasting solutions for diverse solar energy applications.