Iran Energy Association (IEA)Journal of Energy Management and Technology2588-337210220260401From Building to Neighborhood: Investigating the Use of Artificial Intelligence and Machine Learning in Energy Management of Zero Energy Urban Neighborhoods Using a Bibliometric Approach (2020-2025)869824439810.22109/jemt.2026.574220.1588ENMahmoodAbdoosSchool of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, IranHosseinYousefiDepartment of Renewable Energy and Environment, Faculty of New Sciences and Technologies, University of Tehran0000-0002-6372-5127AmiraliSaifoddinSchool of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran0000-0002-9996-5886YounesNoorollahiSchool of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, IranJournal Article20260208Net Zero Energy Buildings (NZEBs) and nearly Zero Energy Buildings (nZEBs) have evolved from individual building concepts toward broader applications at the urban neighborhood scale. This paper presents a bibliometric analysis of energy management practices in zero energy urban neighborhoods, focusing on emerging research trends over the period 2020–2025. Using VOS viewer software, we analyze 584 peer reviewed articles retrieved from major scientific databases. The study maps the co occurrence of keywords, country level research activity, and author collaboration networks. Results indicate exponential growth in publications from 2023 onward, driven by the convergence of zero energy concepts with artificial intelligence, machine learning, deep learning, and digital twins. Key thematic clusters include energy management systems, HVAC optimization, renewable energy integration, smart grids, and the Internet of Things. Geographically, research leadership has shifted from Europe and the United States (2021–2022) to Asian countries (China, India, South Korea, Japan, Iran) by 2023–2025. A persistent research gap is identified: zero energy neighborhood development in hot and humid climates, particularly in developing economies, remains severely underrepresented due to high upfront costs and long payback periods. The review synthesizes design hierarchies passive strategies first, followed by efficient active systems, then on site renewable generation and discusses the limitations of current AI applications, including data availability, generalizability, interpretability, and the absence of standardized benchmarks.https://www.jemat.org/article_244398_eb8d1750f6bebfa505fa270703bce953.pdfIran Energy Association (IEA)Journal of Energy Management and Technology2588-337210220260401Adaptive Super-Twisting Controller for Voltage Regulation in a DC Microgrid9911324591510.22109/jemt.2026.555273.1577ENMehdiAshtari MahinDepartment of Electrical Engineering and Renewable Energy
Research Centre, Damavand Branch, Islamic Azad
University, Damavand, Tehran, IranSeyed MehdiHakimiDepartment of Electrical Engineering and Renewable Energy Research Centre, Damavand Branch, Islamic Azad University, Damavand, IranSeyed VahidNaghaviDigital Transformation Center, Research Institute of Petroleum Industry, Tehran, IranVahidBehnamgolDepartment of Electrical Engineering and Renewable Energy Research Centre, Damavand Branch, Islamic Azad University, Damavand, IranJournal Article20251024Microgrids (MGs) typically function in two operational modes: on-grid and off-grid. The primary challenges in the off-grid mode involve voltage and frequency fluctuations or instability. To address these issues, an appropriate control system is required to regulate the microgrid’s voltage and frequency. Considering the system’s nonlinear characteristics, parameter uncertainties, and load variations, the use of nonlinear and robust control techniques is deemed an effective strategy. In this study, a nonlinear adaptive super-twisting controller based on sliding mode control (SMC) theory is developed to manage the microgrid. The main motivation for employing this controller is its ability to mitigate distortions under parameter uncertainties. To this end, the dynamic equations of the system are first derived, and subsequently, a controller is designed using the principles of SMC to ensure overall system control. To overcome the chattering phenomenon, an adaptive super-twisting algorithm is proposed. The closed-loop stability of the system is verified through the Lyapunov stability theorem. The effectiveness of the proposed method is assessed using MATLAB/Simulink simulations, and its performance is compared with conventional control approaches. The results demonstrate that the proposed adaptive super-twisting controller offers superior performance and practical applicability compared to traditional methods. The proposed controller is evaluated in an islanded (off-grid) DC microgrid configuration under load steps, irradiance variations, and battery parameter uncertainties. Simulation results demonstrate superior voltage regulation compared to a conventional super-twisting sliding mode controller and a PI controller, achieving a settling time below 0.18 s, an overshoot below 0.4%, and an RMSE of 0.42 V.https://www.jemat.org/article_245915_1e98973fd7ae4d59bc937d1f953a1d0b.pdfIran Energy Association (IEA)Journal of Energy Management and Technology2588-337210220260401Peer-to-Peer Joint Energy and Reserve Market with Product Differentiation: Centralized Versus Decentralized11412424459410.22109/jemt.2026.540360.1569ENAtaAjoulabadiDepartment of Electrical Engineering, Faculty of Engineering, Azarbaijan Shahid Madani University0000-0003-4413-2219SajadNajafi RavadanaghDepartment of Electrical Engineering, Faculty of Engineering, Azarbaijan Shahid Madani University0000-0002-9468-9990JavadSalehiDepartment of Electrical Engineering, Faculty of Engineering, Azarbaijan Shahid Madani UniversityJournal Article20250820In modern smart grids, prosumers and distributed energy resources increasingly participate in local electricity markets, while higher renewable penetration raises the need for reserve capacity and operational flexibility. This paper proposes a joint peer-to-peer (P2P) market framework for energy and reserve trading with product differentiation, in which agents negotiate bilaterally with neighboring peers and can trade both energy and reserve capacity. The resulting social-cost minimization problem is formulated and solved using a centralized benchmark together with ADMM-based decentralized and distributed clearing mechanisms. In the fully decentralized market, a coordinator-free consensus ADMM is adopted for direct P2P trading, whereas the hybrid framework employs an exchange ADMM to clear intra-community trades prior to decentralized negotiations across communities. Numerical studies quantify the trade-off between centralized and decentralized clearing in terms of objective value, computation time, and communication burden. The results further demonstrate how product-differentiation coefficients influence market prices and traded quantities. Finally, two coefficient-setting strategies are introduced to steer decentralized market outcomes toward centralized-like or hybrid-like operating points, thereby enabling the market operator or platform designer to influence trading behavior under decentralized clearing.https://www.jemat.org/article_244594_f494fa5ddcfb713c3f9f35566ad0f080.pdfIran Energy Association (IEA)Journal of Energy Management and Technology2588-337210220260401Assessment of Window Energy Transfer Performance in Courtyard Buildings Using DesignBuilder in Tehran’s Climate12413224717010.22109/jemt.2026.566736.1585ENShaghayeghBaratiDepartment of Mechanical Engineering, WT.C., Islamic Azad University, Tehran, IranPeymanEbrahimi NaghaniDepartment of Mechanical Engineering, WT.C., Islamic Azad University, Tehran, Iran.Seyed AlirezaZolfaghariDepartment of Mechanical Engineering, University of Biriand,Journal Article20251217Glazing selection is a key strategy for improving building energy performance; however, its application in heritage buildings with architectural preservation constraints remains to be explored. This study addresses this gap by evaluating 11 glazing configurations, including single, double, and triple-pane systems with varying U-values (0.78–5.82 W/m²K), Solar Heat Gain Coefficients (0.31–0.90), and Visible Transmittance values (0.43–0.91), in a historic courtyard building located in Tehran’s hot-dry climate. Dynamic simulations were conducted using DesignBuilder, based on a Typical Meteorological Year (TMY) weather file for Tehran (Mehrabad station). The model was validated using ASHRAE Standard 140 (BESTEST) benchmark cases. Results show a consistent seasonal pattern across scenarios, although significant differences emerged during peak heating and cooling periods. The triple-pane low-E argon-filled glazing (T2) achieved the best performance, reducing annual energy consumption by 12.7% compared to the base case. In contrast, the single-pane low-iron glazing (S3) increased the energy demand by 1.2% owing to higher solar heat gains. The sensitivity analysis of building orientation indicated that the total annual energy consumption varied between approximately 85,847 kWh and 91,967 kWh, with the lowest demand observed at a 180° orientation. The economic analysis showed that the D4 double Low-E glazing system achieved a shorter payback period of approximately 6 years, whereas T2 required about 10.7 years to recover the initial investment. Overall, the findings demonstrate that reducing the U-value and Solar Heat Gain Coefficient is the most effective strategy for improving building energy performance in hot-dry climates, particularly for heritage buildings, where design modifications are limited.https://www.jemat.org/article_247170_0d6758e06f01df9373fdd3cd6dc13c17.pdf