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Long-range wireless connectivity technologies for sensors and actuators open the door for a variety of new Internet of Things (IoT) applications. These technologies can be deployed to establish new monitoring capabilities and enhance efficiency of services in a rich diversity of domains. Low energy consumption is essential to enable battery-powered IoT nodes with a long autonomy. This paper explains the challenges posed by combining low-power and long-range connectivity. An energy breakdown demonstrates the dominance of transmit and sleep energy. The principles for achieving both low-power and wide-area are outlined, and the landscape of available networking technologies that are suited to connect remote IoT nodes is sketched. The typical anatomy of such a node is presented, and the subsystems are zoomed into. The art of designing remote IoT devices requires an application-oriented approach, where a meticulous design and smart operation are essential to grant a long battery life. In particular we demonstrate the importance of strategies such as “think before you talk” and “race to sleep”. As maintenance of IoT nodes is often cumbersome due to being deployed at hard to reach places, extending the battery life of these devices is critical. Moreover, the environmental impact of batteries further demonstrates the need for a longer battery life in order to reduce the number of batteries used.

Energy modeling and cutting parameter optimization of the machining process have been recognized as powerful and effective ways to save energy. However, in the actual machining process, technologists often use empirical methods to determine the final cutting parameters. Due to the lack of theoretical support and optimization tools, this method is difficult to fully consider the constraints of machine tool capability, cutting tool performance, and workpiece material, which affects the overall performance of the machine tool to give full play. To address this problem, a multi-objective parameter optimization method of computer numerical control (CNC) plane milling for sustainable manufacturing was proposed in this paper. More specifically, three tasks were carried out: (1) an accurate milling energy model considering transient processes such as spindle acceleration was established; (2) a multi-objective parameter optimization model of CNC plane milling was established with cutting parameters as optimization variables and considering various complex constraints; (3) by drawing 3D surface maps, the internal relationship between the cutting parameters and the optimization index was presented in detail and intuitively. Finally, a case study was carried out in the XHK-714F vertical machining center. The results showed that the processing efficiency is improved by 21.0%, the energy consumption is reduced by 15.3%, and the surface roughness is reduced by 5.5% through the optimization of cutting parameters, which verified the effectiveness and feasibility of the proposed model and method.

In this work, we have studied how the vertical illuminance of the human eye position, illuminance of the horizontal work surface, and the brightness of the computer screen in the office space lighting are correlated under an energy-saving environment. This investigation was conducted in a full-scale laboratory that simulates an office space with 20 adults. It was found that when the indoor ambient lighting illuminance changes, the vertical illuminance of the subject’s eye position is affected accordingly, and the two factors are strongly correlated. On the other hand, when the surrounding environment is brighter and the vertical illuminance increases, the illuminance of the horizontal working surface adjusted by the subject during the visual display terminal (VDT) operation is significantly reduced. The horizontal illuminance value can even be lower than the value frequently employed in various countries around the world, since the computer screen brightness will be adjusted accordingly. Therefore, in an energy-saving environment, the illuminance of the horizontal working surface and the brightness of the computer screen adjusted by the users will vary with the ambient lighting. Especially in the current mainstream VDT operating environment and within a certain range of conditions, the interior setting can be lower than the current horizontal illuminance benchmark for additional energy conservation.

This paper proposes the elaboration model of energy requirement prediction taking into account the power of standby, spindle rotation in non-load, feeding, and rapid movement in X, Y, Z+, and Z− axially, and specific energy consumption (SEC) in the X and Y cutting directions, respectively, which could not be considered complete in other models. Each part energy of specific machine tools could be obtained through little experiments for identifying the relationship between energy and tool path with cutting parameters. The method is validated by 27 trial cutting experiment in X and Y cutting directions in the VMC850E machine; the results show that the SEC in the X and Y cutting directions is different. Moreover, it is found that spindle power should be piecewise linear representation according to spindle speed characteristic, due to the correlation coefficient of power model only has 25.45% without segmented. Additionally, the correlation coefficient of the improved SEC model could reach more than 99.98% in each segment. The contribution of this paper is mainly the elaboration energy consumption model considering the cutting direction, which is an efficient approach for predicting energy consumption through tool path to achieve sustainable production in manufacturing sectors.

Abstract With the increase of residents’ requirements for the living environment, the current indoor thermal environment cannot meet the needs of modern rural residents who live in the northeast of Sichuan, China. Passive energy-saving strategies can not only improve the thermal performance of envelopes but also create high economic benefits. Evaluating the application effect of passive energy-saving strategies for traditional dwellings can provide a guide for local residents and policy makers to select rational passive strategies. Seven energy-saving strategies are proposed based on the current local building construction and heat transfer model, and then their energy-saving potential is evaluated by using EnergyPlus and the dynamic investment payback period method. Results show that adding exterior envelope insulation and setting on-top sunspaces on the roof simultaneously can save 83.9% of building energy consumption. However, the most economic energy-saving strategy is only employing exterior envelope insulation for local traditional dwellings when considering the economy. It can save 842 CNY/m2 during 100 years and its dynamic investment payback period is 14.1 years. In addition, building orientation also affects the energy-saving effects and the energy-saving rate can be increased by 8.4% under the optimal orientation (facing south) compared with the worst orientation (facing west).

Battery-powered electric vehicles (EVs) have a limited on-board energy storage and present the problem of driving mileage anxiety. Moreover, battery energy storage density cannot be effectively improved in a short time, which is a technical bottleneck of EVs. By considering the impact of traffic information on energy consumption forecasting, an energy-saving path planning method for EVs that takes traffic information into account is proposed. The modeling process of the EV model and the construction process of the traffic simulation model are expounded. In addition, the long-term, short-term memory neural network (LSTM) model is selected to predict the energy consumption of EVs, and the sequence to sequence technology is used in the model to integrate the driving condition data of EVs with traffic information. In order to apply the predicted energy consumption to travel guidance, a road planning method with the optimal coupling of energy consumption and distance is proposed. The experimental results show that the energy-based economic path uses 9.9% lower energy consumption and 40.2% shorter travel time than the distance-based path, and a 1.5% lower energy consumption and 18.6% longer travel time than the time-based path.

Traditional energy-saving optimization of shop scheduling often separates the coupling relationship between a single machine and the shop system, which not only limits the potential of energy-saving but also leads to a large deviation between the optimized result and the actual application. In practice, cutting-tool degradation during operation is inevitable, which will not only lead to the increase in actual machining power but also the resulting tool change operation will disrupt the rhythm of production scheduling. Therefore, to make the energy consumption calculation in scheduling optimization more consistent with the actual machining conditions and reduce the impact of tool degradation on the manufacturing shop, this paper constructs an integrated optimization model including a flexible job shop scheduling problem (FJSP), machining power prediction, tool life prediction and energy-saving strategy. First, an exponential function is formulated using actual cutting experiment data under certain machining conditions to express cutting-tool degradation. Utilizing this function, a reasonable cutting-tool change schedule is obtained. A hybrid energy-saving strategy that combines a cutting-tool change with machine tool turn-on/off schedules to reduce the difference between the simulated and actual machining power while optimizing the energy savings is then proposed. Second, a multi-objective optimization model was established to reduce the makespan, total machine tool load, number of times machine tools are turned on/off and cutting tools are changed, and the total energy consumption of the workshop and the fast and elitist multi-objective genetic algorithm (NSGA-II) is used to solve the model. Finally, combined with the workshop production cost evaluation indicator, a practical FJSP example is presented to demonstrate the proposed optimization model. The prediction accuracy of the machining power is more than 93%. The hybrid energy-saving strategy can further reduce the energy consumption of the workshop by 4.44% and the production cost by 2.44% on the basis of saving 93.5% of non-processing energy consumption by the machine on/off energy-saving strategy.

Thermal comfort in educational environments affects not only students’ well-being but also their concentration and academic performance. In the context of climate change, university classrooms in Mediterranean climates face particular challenges due to higher and more variable temperatures. This study evaluates thermal comfort in classrooms in southern Portugal, comparing natural ventilation (NV) and air-conditioning (AC) modes. Through environmental measurements and student surveys, thermal perceptions, preferences and factors such as position within the classroom were analysed. The results reveal that NV classrooms offer sustainable benefits, but their effectiveness decreases when outside temperatures exceed 28 °C, increasing thermal discomfort. In contrast, AC classrooms maintain more stable and comfortable conditions, although they have thermal gradients that affect specific zones, such as areas near windows or air vents. This study highlights the need for hybrid strategies that prioritise NV in moderate temperatures and use AC as a support in extreme conditions. Furthermore, it underlines the importance of appropriate architectural design and specific adaptive models for Mediterranean climates, balancing thermal comfort and energy efficiency.

The global fight to mitigate greenhouse gas emissions and address climate change demands that firms implement energy-saving strategies while maintaining firm financial performance. However, the impact of energy efficiency on corporate financial performance remains underexplored, especially in South Africa. This study applied a two-step system generalized method of moments (SGMM) to explore the impact of energy efficiency on the financial performance of higher polluters and emitters listed on the Johannesburg Stock Exchange (JSE) over the period from 2015 to 2023. The sample for the study was 58 companies listed on the JSE. The data was sourced from the firm’s annual reports covering the period of 9 years (2015–2023). Our study reveals no significant association between energy-saving strategies and firm financial performance within high-polluting and emitting firms listed on the JSE. Notably, the study reports that leverage positively affects both firm profitability and market valuation, suggesting that debts may serve as a dynamic capability for improving firm performance if it is used strategically. Our findings underscore the importance of mandatory independent assurance of ESG reports to mitigate greenwashing risks.

Block morphology refers to critical parameters influencing building energy performance on the block scale. However, analysis of the combined effect of block morphological parameters on building energy consumption with real blocks is lacking. In this paper, the aim is to evaluate the combined effect of office block morphology on building energy consumption in the context of the Hot-summer and Cold-winter zone in China. First, a workflow for the energy assessment of office buildings with the coupled block morphology on the block scale was proposed with evaluation tools. Seventy office blocks in Wuhan were taken as examples and then classified based on building layout typology and building height. Afterwards, the morphological parameters and building energy use intensity (EUI) for different blocks were calculated. Then, the combined effect of block morphology on the buildings’ energy consumption was evaluated and the model on predicting the building energy consumption of office blocks was proposed. Finally, based on the results, low-energy design strategies were projected for office blocks. The results illustrated that the effect of block morphology on building cooling, heating, and lighting is EUI 28.83%, 28.56%, and 23.23%, respectively. Building shape factor (BSF), floor area ratio (FAR), average building height of block (BH), and average building depth of block (BD) are effective block morphological parameters. The key morphological parameters which combined affect the building energy consumption of office blocks are BSF and FAR; BSF has 1.24 times the effect on building energy consumption than FAR. The workflow built in this paper can be applied to other cities around the world for promoting sustainable cities.