Robotic tectonics have been integrated into the architectural profession through automated construction for more than a decade, advancing sustainability initiatives in the industry and increasing the quality of building construction. Over the years, avant-garde architects have explored the feasibility of this new design paradigm through the integration of newly-developed digital design software into automated construction. This robotic digital workflow continues to push designers to re-think the complete architecture process (from design conception to physical construction) and guides the building industry towards more precise, efficient, and sustainable development. However, in the current environment of architectural education, professional courses can be fragmented, thematic, and overly academic. Such content is not inherently compatible with the latest technological developments. The lack of understanding and application of digital technological can subsequently lead to the lack of sustainable development in architectural education. In this paper, we aim to introduce a new didactic pedagogical approach that is reliant on the principles of robotic tectonics and is defined through linear development in four distinct, developmental stages (based on information gleaned from four “Robotic Tectonics” workshops and various other rich teaching practices). This pedagogical framework provides interdisciplinary knowledge to architecture students and enables them to use advanced digital tools such as robots for automated construction, laying the groundwork for the discovery of new and complex building processes that will redefine architecture in the near future.

Three-dimensional (3D) information technology has become an important technical support in digital heritage preservation practice. However, due to the lack of systematic quantitative research, it is difficult to form a comprehensive understanding of the historic and cultural districts, from macro to micro. Our study aimed to establish a systematic 3D spatial diagnostic framework combining 3D scanning and SPSS data descriptive analysis and regression analysis for historic and cultural districts to promote sustainable historic and cultural area preservation. Taking Zhongshan Street in Qi County as an example, data statistical analysis was carried out on morphological feature data from the macro level of the district, the meso level of architecture, and the micro level of elements. The research conclusion shows that at the macro level the street form continues the main features of a traditional alley spatial skyline, height–width ratio, and sectional symbol language. At the meso level, the architecture reveals various periods of style in terms of the facade width and mathematical relationship between traditional architectural facades. At the micro level, architectural detailing explains the main reasons for the recent new construction being inconsistent with the historic and cultural district appearance. This quantitative diagnostic method can accurately analyze the current characteristics of historic and cultural districts and easily provide effective suggestions for follow-up preservation methods.

The increasing reliance on photovoltaic (PV) generation as a cornerstone of carbon neutrality has led to transformative changes in the energy structure, further impacting electricity market trading mechanisms and price volatility. The electric power system reform also promoted wholesale trading in the Japan Electric Power Exchange (JEPX) spot market. This study explores an effective JEPX spot market price forecasting model that enables PV power suppliers to make informed production decisions and ensure revenue optimization. We found that understanding the net demand (total demand minus PV generation) is crucial for accurate price forecasting, as it allows for a more precise reflection of the gradual evolution of the solar-dominated energy structure of the dynamic electricity demand. We also conducted parameter tests and a comparative analysis of different training loops and periods in the basic form using an artificial neural network (ANN) and support vector regression (SVR) algorithms. The results indicated that the narrow ANN and SVR models with a linear kernel function and training in the continuous loop method performed better in spot market price forecasting than other model settings. Our proposed approach can provide essential insights into future price trends, facilitating informed sustainable energy planning and resource allocation for power generation to guarantee the benefits of achieving solar promotion and net-zero transition.

The essence of sustainable urbanization is to take a holistic approach to the harmonious development of economic, social, cultural and environmental protection. This paper applies the urban sustainability assessment system to analyze the characteristics of indicators related to the quality of the built environment and environmental pressure of 91 cities in four major megalopolises in China from 2010 to 2018. It also combines statistical methods to summarize the general features of urban development through a comprehensive urban performance evaluation by comparative and classification analysis for the purpose of scientific guidance on sustainable urbanization. The comparative results showed that in terms of urban sustainability, the Yangtze River Delta performed best, followed by JingJinJi, Pearl River Delta and Shandong Peninsula. Of which, the quality of built environment in JingJinJi and the environment pressure in the Shandong Peninsula require particular attention to improve and decrease, respectively. Moreover, cities can be grouped into six development types through performance clustering including three positive and three negative types. The characteristics of all types are summarized, and the performance of the specific indicators are detailed compared to serve as a guiding basis for making generic recommendations of sustainable urbanization.

The variation in solar output due to weather hinders both reliability and profitability, particularly because an increase in photovoltaic (PV) penetration reduces daytime electricity prices. In this study, a method to generate a reserve value by managing prediction errors is developed. A model to estimate the error threshold corresponding to a specific cumulative probability is constructed using a polynomial approximation based on the historical distribution of prediction errors at a single site. This threshold is considered as the headroom required to absorb the prediction errors. The model is then applied to multiple sites to evaluate its overall applicability and the smoothing effect of site aggregation. The output is divided into clipped and nonclipped portions under overloading conditions; the former is modeled with a gamma distribution and the latter with a polynomial surface. Two models are tested: one using overall errors and another incorporating conditional probability with the clearness index. The results indicate that site aggregation reduces imbalance events and increases bid amounts. Under overloading, the optimal DC/AC ratio depends on the specified cumulative probability. Future works must focus on improving characterizing prediction errors under overloading and developing methods for predicting clipping to enhance balancing market participation of PV plants.