Noor Jalo, Ida Johansson, Patrik Thollander, Therese Nehler, Stephan Barthel
The benefits of energy efficiency (EE) improvements in industry include multiple energy benefits (MEBs) that have not been studied extensively. Although MEBs are assumed to result from energy efficiency measures (EEMs), they have not been examined simultaneously with EE, creating a knowledge gap that needs to be addressed. This paper investigates MEBs among industrial small and medium-sized enterprises (SMEs) in different regions in Europe. The study suggests that MEBs are social constructs and argues that EEMs and MEBs belong to the same culture, with interactions occurring at macro, meso, and micro levels. This research examines the influence of regional energy policy contexts on SMEs using a questionnaire distributed across five European regions. The analysis considers regional differences and explores how contextual factors influence the prioritization of various MEB categories. The results show that “increased knowledge about energy use in the company” is the top-ranked MEB among the investigated SMEs. Although all regions have supportive energy policies available, there is a tendency to prioritize certain benefit categories over others. Companies in Groningen, the Netherlands, demonstrate stronger engagement with market, competition, and production-related benefits. SMEs in Hessen, Germany, focus mainly on production-related benefits, while companies in Burgenland, Austria, show engagement in organizational, knowledge, production, and waste-related benefits. Meanwhile, companies in La Rioja, Spain, and South-West Oltenia, Romania, do not appear to be engaged in a particular category of benefits but demonstrate strengths in specific areas and noticeable gaps in others. The findings indicate that MEBs are socially constructed outcomes shaped by interactions between regional contexts and company-level practices. The study highlights the importance of considering MEBs alongside EEMs rather than as separate outcomes. Further research examining meso- and micro-level interactions is suggested to broaden the understanding of how MEBs emerge and develop among industrial SMEs.
Hassan Ali Khan, Zahid Wadud, Ghulam Hafeez, Ateeq Ur Rehman, Sandra Corasaniti, Paolo Coppa
Electric energy theft is a major issue for the sustained use of modern smart grids (SGs). It affects the electric system’s overall long-term reliability and affordability. Even though advanced metering infrastructure can gather a large amount of data regarding electric energy utilization, it is quite difficult to address non-technical losses (NTLs). This is because electric energy consumption patterns exhibit high-dimensional characteristics, time-varying features, and are not invariably steady. Adapting the simple machine learning and artificial intelligence approach generally requires a single multidimensional electric energy consumption estimation and topical feature extraction mechanism, a limitation that prevents it from characterizing long-range temporal dependencies and intermittent behavior. Consequently, such procedures tend to have lower detection and elevated rates of false positives. In this regard, in this research, a novel transformer-based wide and deep convolutional neural network (TWiDeCNN) is proposed to efficiently identify electric energy theft in a scenario based on SGs. The suggested TWiDeCNN model is trained and tested on a real-world dataset of electric energy use collected from working SG environments, which makes it useful in the real world. Experimental results show that the suggested model works better than modern advanced methods that use binary classification metrics. These results show that the model performs well, is stable, and can be used at a large scale. They also show that it could be used in cutting-edge ways to improve energy management and find more electric energy theft. In-depth simulation results on the State Grid Corporation of China (SGCC) dataset demonstrate that TWiDeCNN outperforms the original wide model. In addition, it outperforms deep convolutional neural network (CNN) and other benchmarks in terms of ranking capability and detection performance with MAP@100 of 96.00 % and MAP@200 of 93.58 % at a 70% split. Moreover, parameter sensitivity analysis shows that proper tuning of parameters α, β, γ, and R would help the model build a deep feature-based, powerful representation and a better adaptation ability. Our proposed model maintains robust performance, demonstrating its stability and effectiveness for electric energy theft detection.
Sustainability has attracted immense attention in the built environment, increasing calls for further motivation to accelerate the adoption of clean energy technologies by property stakeholders. Highlighting the financial benefits of green initiatives from investors' perspectives will encourage or maintain efforts towards a sustainable property sector. The study analyses key annual stock market indices such as Earnings per Share [EPS], Price-Earnings Ratio [PE], Market-To-Book Ratio [MTB], Net Tangible Asset [NTA] per share, and Dividend Yield [DY] to assess the financial performance and risk profile of equity holders in a sample of 17 A-REITs before and after the implementation of the Commercial Building Disclosure Program [CBDP]. This is supplemented by a fixed effects regression model to estimate the drivers of dividend yield. There is a general rise in EPS and PE, along with stability in MTB and NTA, following the implementation of the CBDP legislation. This indicates a boost in the financial performance of REITs and a reduction in stock earnings volatility after CBDP. From our fixed effects regression model, improved market capitalisation increases dividend yield, while asset depreciation does not. Using stock market indices provides a clearer and more practical understanding of the financial gains from green investments. These findings could guide the decisions of current and prospective equity investors in the REIT market, REIT managers, policymakers focusing on sustainability, and advocacy groups.
Manufactured sand (M-Sand) is a low-cost granular medium for sensible heat storage and characterising the internal temperature field during charging requires either dense instrumentation or a physics-consistent model. In a previous experimental study, an M-Sand packed-bed thermal energy storage unit was characterised at three flow rates – 1.00, 1.88, and 2.66 LPM–using five internal thermocouples. A physics-informed neural network was trained on this dataset while enforcing the three-dimensional transient heat conduction equation through a physics-constrained loss term. The trained model predicted temperature as a continuous function of space, time, and flow rate. The model achieved R 2 = 0.9616, RMSE = 1.493 °C, and MAE = 0.984 °C across all sensor locations and flow rates. Spatiotemporal maps showed the thermal front advanced faster laterally than axially, with an estimated front velocity of 1.8 × 10 − 5 m/s in the z-direction, consistent with the thermal diffusivity of M-Sand (α = 1.538 × 10 − 7 m 2 /s). Peak temporal heating rate reached 1.2 °C/min near the tube surface. Spatial temperature gradient maps showed peak gradients of 620 °C/m at the tube legs. Three-dimensional isosurfaces captured the progression from tube-adjacent lobes to volume-filling surfaces. The results confirm that a PINN trained on sparse experimental data from five thermocouples and three flow rates can reconstruct the full three-dimensional thermal behaviour of the M-Sand bed within the training operating functioning as a physics-consistent surrogate model that reduces the need for dense sensor instrumentation in packed-bed Thermal energy storage characterisation.
Electricity demand in Sub-Saharan Africa is rising, and in Nigeria, over 40% lack access to electricity. A photovoltaic-thermal (PV-T) system was simulated in TRNSYS, using shea butter for active cooling of photovoltaic (PV) modules. Its performance was compared with that of conventional paraffin wax, and the effects of system design parameters were examined. Efficiency gains from using shea butter and paraffin wax were 0.18% and 0.03%, respectively, during peak solar hours. Notably, shea butter reduced cell temperatures by 1.75 °C during peak sunlight compared with paraffin wax. Peak electrical power and efficiency reached 130.53 W and 11.35%, versus 109.78 W and 9.10% for conventional PV. The optimal flow rate was identified as 2.0 LPM, with a shea butter layer thickness of 0.04 m to optimise thermal capacity and heat transfer. Environmentally, shea butter is renewable and biodegradable, with a lower carbon footprint of 2.20 kg CO 2 /kg compared with 3.78 kg CO 2 /kg for paraffin wax, according to CarbonCloud data. As a bio-based energy storage medium, shea butter not only reduces global warming potential but also supports the SDGs, including affordable energy, climate action, and responsible consumption.
