Background: University libraries are among the most energy-intensive spaces within Nigerian academic campuses, operating extended daily hours and housing power-demanding equipment including server rooms, lighting arrays, reprographic facilities, and climate-control systems. Despite their substantial contribution to institutional operations, the environmental footprint of these buildings — expressed through institutional greenhouse gas (GHG) emissions — remains poorly quantified across Nigeria’s six geopolitical zones. This study investigates carbon emissions in federal and state university libraries across Nigeria by examining energy-use patterns, emission sources, and the structural barriers hindering decarbonisation in these regions. Methods: Employing a concurrent (convergent) mixed-methods design, this study surveyed 24 university libraries — 12 federal and 12 state institutions, four purposively selected from each of Nigeria’s six geopolitical zones — between January and June 2025. Quantitative energy-inventory and Likert-scale barrier data and qualitative open-ended responses were collected concurrently and integrated at the interpretation stage. Data were collected through a self-developed energy-inventory questionnaire and structured facility checklists administered to a designated primary respondent (the University Librarian or deputy) at each library, supplemented by input from unit heads and maintenance officers where relevant. Emission sources were classified according to the GHG Protocol Corporate Standard across Scopes 1, 2, and 3, applying an operational-control approach to define the library’s organisational boundary. Results: Diesel-powered generators constituted the largest source of Scope 1 emissions in the sampled libraries, accounting for an estimated 42-68% of direct emissions, reflecting the combined effect of constant generator use and unreliable grid electricity supply. Fluorescent and incandescent lighting remained predominant in 79% of surveyed libraries, and air-conditioning units in 65% of facilities were older, higher-energy-intensity models. Descriptively large inter-zonal differences were observed — South-West zone libraries showed comparatively higher adoption of energy-saving technologies and on-site solar generation, whereas North-East zone libraries reported the most constrained infrastructure — although, given the small per-zone sample (n = 4), these differences should be interpreted as indicative rather than confirmatory. Institutional and financial budgetary constraints were the most highly rated perceived barrier to decarbonisation, followed by the absence of institutional energy policy and inadequate staff awareness. Conclusions: The findings point to the value of a coordinated national framework for integrating energy-efficiency standards into university library management, active stakeholder participation, and phased adoption of renewable energy technologies to reduce the carbon footprint of academic libraries in Nigeria, in line with the country’s Nationally Determined Contributions and Sustainable Development Goal 13. Because the sample was purposively — not randomly — selected, these findings should be read as an in-depth, geographically diverse case comparison rather than as statistically generalisable to all Nigerian university libraries.
This study presents a quantitative assessment of early stabilization losses and hotspot-induced degradation in polycrystalline photovoltaic (PV) modules following IEC 61215 procedures. Six new 100 W modules were subjected to controlled outdoor stabilization under cumulative irradiance exposures of 5 and 10 kWh/m2, while ten aged 260 W modules exposed for two years under open-circuit (Voc) conditions were evaluated under induced partial-shading hotspot stress. Electrical performance was characterized under standard test conditions (STC) using I-V measurements, complemented by infrared thermography and visual inspection. Stabilization testing revealed maximum power (Pmax) reductions of 3-5% after 5 kWh/m2 and up to 10% after 10 kWh/m2 exposure, with fill factor decreases of approximately 1-2%. In aged modules, hotspot activation produced localized temperatures exceeding 90 °C, surpassing the IEC acceptance threshold of 85 °C. Post-stress measurements showed irreversible Pmax losses ranging from 8% to 17%, accompanied by short-circuit current reductions of up to 4% and visible encapsulant browning and interconnect degradation. The combined effects indicate potential cumulative output losses exceeding 20% relative to nameplate ratings. These results emphasize the importance of rigorous stabilization and hotspot qualification testing to ensure long-term reliability, safety, and energy yield of PV modules operating under real-world conditions.
In this study, a framework combining multiple deep learning techniques is proposed for electrical equipment state recognition and monitoring to address the problem that existing methods have limited feature extraction capabilities under high noise and complex working conditions. The adaptability of the model is improved through data augmentation and self-supervised contrastive learning. A hybrid architecture of CNN-BiLSTM and Transformer is designed to extract spatiotemporal features, and the model performance is optimized by combining domain adaptation technology, neural architecture search (NAS), and deformable convolutional network (DCN). The experimental data comes from a large-scale electrical equipment monitoring system in an industrial park in a certain province, covering 15 equipment states and a total of 269,000 multimodal data. The experimental results show that the proposed method is significantly superior to the baseline model in terms of recognition accuracy (95.37%), real-time performance (detection delay of 3.02ms), and cross-domain adaptability (improved by 41.5%), providing an efficient and reliable solution for electrical equipment state monitoring, which has important theoretical and practical application value.
Driven by the global energy crisis and carbon neutrality goals, Building-Integrated Photovoltaic (BIPV) systems are increasingly recognized as a key solution in sustainable architecture. This study presents a comprehensive review of BIPV integration approaches and emerging performance strategies at the building scale. Combining qualitative analysis with bibliometric methods, the research is based on publication data from the Web of Science Core Collection, using relevant topics in photovoltaic technology and green building, and covering the years 2001 to 2025. The paper traces the evolution of BIPV technologies, identifies research hotspots, and reveals shifts in structural and performance paradigms. Technically, BIPV systems have evolved from simple energy-generating add-ons to multifunctional building envelope components that integrate structural, protective, visual, and environmental regulation functions, with applications in roofs, façades, shading systems, and more. Performance assessment has become increasingly multi-dimensional, encompassing energy conversion efficiency, thermal management, environmental impact, aesthetic integration, and economic viability. Bibliometric analysis reveals a transition from structural integration to multi-objective performance optimization, with smart modeling, AI-assisted design, and urban energy network integration emerging as future directions. Despite this progress, BIPV implementation still faces systemic barriers such as structural complexity, high costs, regulatory fragmentation, and limited interdisciplinary collaboration. In response, this paper proposes a framework addressing intelligent modeling, policy incentives, standard development, and design education to advance the deep integration and widespread adoption of BIPV in green buildings. The findings aim to provide theoretical insights and practical guidance for BIPV deployment, policymaking, and future research.
In view of the problem that the current power grid dispatching voice interaction system is not adaptable enough to the terminology specific to the power grid dispatching field and is easily disturbed by environmental noise, resulting in command recognition errors and missing keywords, this paper constructs a deep fusion model based on Wav2Vec 2.0 self-supervised pre-training and Conformer structure, aiming to achieve Automatic Speech Recognition (ASR) and optimize accuracy. First, based on the Wav2Vec 2.0 model, the original dispatching voice signal is self-supervised pre-trained to extract features and capture its low-level time domain and frequency domain expressions. Then, the extracted voice features are input into the Conformer structure fine-tuned by the dispatching field corpus to achieve high-precision modeling of long-distance context. Finally, the power grid professional terminology dictionary is embedded in the decoding stage, and the spectrogram enhancement and background noise synthesis mechanism are combined to achieve end-to-end joint optimization. The results showed that the accuracy, recall, and F1 score of the speech recognition model in this article were 92.3%, 89.1%, and 90.7%, respectively, with an average of Word Error Rate (WER), Character Error Rate (CER), Weighted WER were 10.8%, 5.7%, and 13.8%, respectively; The F1 score for term recognition reached 90.7%; The recognition rate of Top-3 is above 0.75, and the complete recognition rate of instructions reaches 84.6%. Under extreme low signal-to-noise ratio conditions of -5dB, its WER control is 42.1%. The conclusion shows that the method proposed in this paper can effectively improve the accuracy and scene adaptability of ASR, provide reliable support for high-precision voice interaction in power grid scheduling, help improve the safety and reliability of power facility operations, and reduce work delays caused by misoperation or poor communication.
