Historical Power Research Articles

Based on Deep Learning Model and Flink Streaming Computing Short Term Photovoltaic Power Generation Prediction for Suburban Distribution Network

With the advancement of global energy internet construction, accurate prediction of new energy generation power such as photovoltaic is an important foundation for ensuring the safety and economic working of new power systems. A short-term photovoltaic power generation prediction method for suburban distribution networks based on deep learning model fusion and Flink flow calculation is proposed to address the challenges of complex power grids, diversified disturbance factors, and isolated monitoring points. This method uses Bi directional Long Short Term Memory(BiLSTM) to extract cross sequential nonlinear characteristic of photovoltaic power generation time series data. Compared with standard LSTM, BiLSTM can consider both historical and future information simultaneously, thus extracting richer extracted features from power generation time series data. This method also integrates attention mechanism to capture the importance distribution of historical temporal features for power generation prediction, effectively solving the problem of long-term temporal dependence in standard LSTM models. The Flink streaming computing framework embeds a trained BiLSTM-Attention photovoltaic power generation prediction model, enabling real-time prediction and monitoring analysis of photovoltaic power generation at various monitoring points in the suburban distribution network. This article uses a dataset of a suburban photovoltaic power station for validation, and trains the model with historical power generation data, meteorological factors, weather types, seasons, and other information as inputs. The BiLSTM-Attention fusion model studys the temporal characteristics of power generation, and has high accuracy in predicting short-term photovoltaic power generation in different scenarios. The Flink streaming computing platform can not only process high throughput predicted power data, but also has low time delay.

Decentering governance models: education policy and school accountability in Post-Devolution Wales

ABSTRACT This paper investigates education policy reform in post-Devolution Wales with a focus on school accountability. Specifically, it provides a historicist analysis of Wales’ decision to abandon publicly mandated school league tables and subsequent accountability reforms. The paper uses the literature on four governance models to investigate successive accountability regimes in Wales through time. These have changed from a Trust and Altruism model, via a Transparent Public Rankings model, to a new, emerging model that relies on self-assessment and collaborative accountability standards. Notably, the Organization for Economic Cooperation and Development (OECD) has played an increasingly important role in shaping this process. Central to our analysis is Mark Bevir and Roderick Rhodes’ decentered theory. The paper argues that governance models need to be understood with reference to the dynamic interplay of historical constraints, power struggles and new belief systems that respond to policy dilemmas. As Wales has embarked on several substantial education policy reform initiatives, the paper demonstrates the importance of a historically informed approach to policy design and implementation which is responsive to competing traditions and beliefs.

Working in a relational way is everything: Perceptions of power and value in a drug policy-making network

BackgroundThe development of drug policies has been a major focus for policy-makers across North America in light of the ongoing public health emergency caused by the overdose crisis. In this context, the current study examined stakeholders’ experiences and perceptions of power and value in a drug policy-making process in a North American city using qualitative, questionnaire, and social network data.MethodsWe interviewed 18 people who participated in the development of a drug policy proposal between October 2021 and March 2022. They represented different groups and organizations, including government (n = 3), people who use drugs-led advocacy organizations (n = 5), other drug policy advocacy organizations (n = 5), research (n = 3) and police (n = 2). Most of them identified as men (n = 8) and white (n = 16), and their ages ranged between 30 and 80 years old (median = 50). Social network analysis questionnaires and semi-structured qualitative interviews were administered via Zoom. Social network data were analysed using igraph in R, and qualitative data were analysed using thematic analysis. The analyses explored perceptions of value and power within a drug policy-making network.ResultsThe policy-making network showed that connections could be found across participants from different groups, with government officials being the most central. Qualitative data showed that inclusion in the network and centrality did not necessarily translate into feeling powerful or valued. Many participants were dissatisfied with the process despite having structurally advantageous positions or self-reporting moderately high quantitative value scores. Participants who viewed themselves as more valued acknowledged many process shortcomings, but they also saw it as more balanced or fair than those who felt undervalued.ConclusionsWhile participation can make stakeholders and communities feel valued and empowered, our findings highlight that inclusion, position and diversity of connections in a drug policy-making network do not, in and of itself, guarantee these outcomes. Instead, policy-makers must provide transparent terms of reference guidelines and include highly skilled facilitators in policy discussions. This is particularly important in policy processes that involve historical power imbalances in the context of a pressing public health emergency.

Two-stage Day-ahead Multi-step Prediction of Wind Power Considering Time-series Information Interaction

With the large-scale development of wind power, high penetration wind power grid connection poses serious challenges to the safe and stable operation of the power system. However, the current accuracy of wind power forecasting is facing bottlenecks due to the limitations of Numerical Weather Prediction (NWP) data. Therefore, this article proposes a two-stage day-ahead multi-step wind power prediction (WPP) scheme that considers temporal information interaction. In the first stage, the next day prediction of wind power is based on historical power and 0∼24 hours NWP data. Then, an embedded deep decomposition module is used to extract predictable components and multi-scale information fusion is performed. In the second stage, the result of day-ahead WPP is obtained based on the extracted predictable components and combined with 24∼48 hours of NWP data. The wind farms in Jilin and Inner Mongolia of China are used to experimental analysis. The results show that the scheme proposed in the article has a better prediction effect compared with other schemes in the paper, which can effectively improve the multi-step prediction accuracy of day-ahead wind power.

Abstract B099: Addressing cancer disparities in the United States-Affiliated Pacific Island jurisdictions: Insights from the Scaling What Works program

Abstract Purpose The Scaling What Works (SWW) program aimed to build the capacity of National Comprehensive Cancer Control Program (NCCCP) recipients to scale three CDC-identified cancer control projects through training, mentorship, and technical assistance (TA). The SWW program held a Symposium to provide in-person training on adapting, implementing, evaluating, sustaining, and scaling the three projects. We recruited 100% of US-Affiliated Pacific Island (USAPI) NCCCP programs (n=6), who made up approximately 26% of all programs that attended the Symposium (n=23). The USAPI jurisdictions face high cancer burden due, in part, to the United States nuclear legacy, the effects of climate change, limited medical infrastructure, and lack of culturally competent, centralized care across the almost 4,000 miles that the USAPI spans. Through the SWW program, USAPI recipients are supported in their ongoing efforts to mitigate cancer disparities affecting their communities. MethodsWe applied community-based participatory research methods and health equity principles to ensure the SWW program was relevant and responsive to the USAPI recipients’ contexts and needs. We created a SWW subcommittee to learn about and advocate for the specific needs of the USAPI. The committee spoke with ten clinicians and researchers with experience working in the USAPI, across four informal meetings, to understand the unique cancer prevention and control context of each USAPI jurisdiction. The committee reviewed USAPI cancer plans and extracted data related to SWW activities, including cancer prevention priorities, community partners, demographic characteristics, and healthcare infrastructure. Data were added to a matrix, coded, and summarized by subcommittee team members to develop a USAPI fact sheet. We shared the fact sheet with SWW Symposium facilitators and mentors to provide them with the necessary information to support the USAPI programs. Findings We identified components critical to providing effective training and TA that will support USAPI-led work to address disparities in cancer prevention and control. These components include 1) acknowledging the current and historical power dynamics that establish the USAPI cancer prevention and control program space, 2) adjusting generalized training content to each jurisdiction’s unique context and cancer prevention goals, 3) adapting communication strategies to USAPI programs’ preferences and time zones, 4) facilitating networking opportunities both between NCCCP USAPI jurisdictions and across all NCCCP recipients to build a SWW community of practice, and 5) establishing a sustained, bidirectional partnership between USAPI program leaders and the SWW team to collaborate on future cancer prevention and control projects and to provide training and TA, as needed. ConclusionsThe lessons learned from SWW regarding health equity and capacity-building can inform the efforts of researchers, evaluators, and training and TA providers working with USAPI jurisdictions, states, and territories engaged in cancer prevention and control work. Citation Format: Amy L. Tran, Mary Wangen, Renée M. Ferrari, Jingle Xu, Alison T. Brenner, Stephanie B. Wheeler, Rebecca J. Lee, Elizabeth A. Rohan, Angela R. Moore, Shixiu Wang, Alyssa LaMonica, Mary Puckett, Amy L. Thompson, CK Smith, Katie Jones, Malesa Pereira, Rogelio Robles-Morales, Lori A. Crane, Courtney N. Petagna, Thuy T. Vu, Betsy C Risendal, Rachel Hirschey. Addressing cancer disparities in the United States-Affiliated Pacific Island jurisdictions: Insights from the Scaling What Works program [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B099.

Reading ideological power representations through space: the tale of two cities Berlin and Ankara

The study explores the entangled relationship between power dynamics and urban spaces particularly governmental spaces. Drawing from Foucault's conceptualization of power as a nuanced interplay of social processes, the research explores Berlin and Ankara's governmental structures and regarding urban plans while unveiling how power is manifested in built environments. However, rather than a comparative analysis, the study presents narratives of these cities since each reflects a unique historical contexts and power dynamics i.e. Berlin, is a historical centre of power, contrasting with Ankara, which presents republic's capital as a modernisation shift. The study uses, descriptive analysis and reveals how power is articulated within urban spaces and societal structures, while crystalising the shifts in political ideologies and their impact on governance structures. Therefore, study aims to investigate the role of urban planning and architecture in shaping power dynamics in relation to ideologies whether represented openly or subtle within the urban realm. The research, following descriptive analysis for the subjected cities, draws on secondary data as its primary source, and interprets the data through the lens of Lefebvre's dialectic of production of space and Dovey's investigations into power dynamics within the built environment, along with other contributions to the urban space and power literature. Ultimately, urban planning and architecture serve as representations of power, shaping societal narratives and identities. The study underscores the significance of understanding these narratives to comprehend the societal impacts of governance structures and power dynamics over time.

The future of higher education and the claim of globalisation: revisioning the past, re-imagining the future

ABSTRACT Recent years have seen a rise in policy papers and academic literature about the future of higher education, often formulated within a particular globalist imaginary. The thesis of this article is that this imaginary configures discourses on higher education futures and simultaneously reconstructs the history of higher education in terms of a process of global isomorphism. The prevailing discourse assumes a transition from nation-state-centred to globally oriented universities, emphasising convergence and integration on a global scale. Critically examining this globalist imaginary, the article uncovers several misconceptions in the historiography of higher education under the guise of globalisation. The globalist perspective tends to cast higher education history within a diffusionist framework, ignoring the complexities and nuances of historical development, especially in contexts marked by histories of colonialism. The article advocates for a cultural historiography that acknowledges contextual differences and confronts historical power relations. Challenging the assumptions of global isomorphism, the authors propose a shift in focus to foster a more nuanced understanding of the past of higher education, enabling more interesting and relevant imaginations of higher education futures.

A potential model of national forest co-management: History of aspen management on the Leech Lake Reservation and Chippewa National Forest in Minnesota

Aspen exists as the most abundant and industrially important forest type in contemporary Minnesota. Historically, the species was seen as economically undesirable yet saw prolific increases in abundance due to pine harvest and fire. The Leech Lake Reservation exists as a unique case study of tribal-U.S. Forest Service co-management, with 90 % of reservation land within Chippewa National Forest boundaries. This large geographical intersection was created by a series of American Indian law policies and makes the relationship between the Chippewa National Forest and the Leech Lake Band of Ojibwe unique within the USDA National Forest System. Comparison of pre-reservation and more modern forest/land cover types within the modern-day boundaries of the Leech Lake Reservation were conducted alongside investigation of historical management authority documentation of Chippewa National Forest and Leech Lake Band of Ojibwe cooperation and broader historical management of aspen in Minnesota. Results suggest historical power dynamics between the Chippewa National Forest and Leech Lake Band of Ojibwe influenced decreasing pine and increasing aspen on the Leech Lake Reservation. However, the modern relationship between the Chippewa National Forest and the Leech Lake Band of Ojibwe shows the potential for successful co-management within the shared goals of aspen management.

Clive Palmer’s claims against Australia for billions renew pressure to remove investor rights to sue governments from trade agreements

Abstract The Australian debate over Investor-State Dispute Settlement (ISDS) sharpened in 2023 because Australian mining billionaire Clive Palmer, having previously registered his mining company in Singapore, has claimed to be a Singaporean investor. He is using ISDS provisions in the 2012 ASEAN-Australia-New Zealand Free Trade Agreement and the amended 2017 Singapore-Australia Free Trade Agreement to sue the Australian government for a total of A$410 billion in three separate claims. This article uses Cox’s critical theory framework developed by Schneiderman to explain the historical development and power dynamics of ISDS, the contest between its business supporters and social movement critics, and the impact of this contest on governments. It then analyses the Palmer claims and explores the global debate about ISDS, including its increased use by fossil fuel companies against government regulation of carbon emissions, which has led to increased resistance from social movements and governments. ISDS is also being reviewed by the United Nations and World Bank institutions which provide arbitrators for its tribunals and by the Organisation for Economic Co-operation and Development. The conclusion assesses the debate over whether ISDS can be reformed and its future viability.

Ultra-short-term Load Forecasting Based on XGBoost-BiGRU

High-precision load forecasting serves as the foundation for power grid scheduling planning and safe economic operation. In scenarios where only historical power load data is available without other external information, fully exploiting meaningful features from the temporal load sequence is crucial for improving the accuracy of load forecasting. Therefore, an ultra-short-term load forecasting method that combines eXtreme gradient boosting (XGBoost) and bidirectional gated recurrent unit (BiGRU) is proposed in this paper. Considering various factors that affect loads, a candidate feature set is established, which includes temporal information and historical loads. XGBoost is used to select the features that contribute significantly to load forecasting, forming an optimal feature set. These optimal features are then used as inputs to the BiGRU, and the bayesian optimization algorithm is applied to optimize the network hyperparameters. Then the load forecasting model for the next 15 minutes based on BiGRU is generated by training iteratively. The proposed XGBoost-BiGRU method is validated on real load data from a province in China. Experimental results demonstrate that the method can effectively avoid the impact of redundant features, improving both prediction accuracy and efficiency. The research has significant importance for guiding real-time supply-demand balance calculations and scheduling in power grids.

3DTCN-CBAM-LSTM short-term power multi-step prediction model for offshore wind power based on data space and multi-field cluster spatio-temporal correlation

The accuracy of offshore wind power forecasting (OWPF) is the basis for guaranteeing the safe dispatch and economic operation of power systems, which can reduce the technical and economic risks faced by power market participants. Combining the theoretical approaches of data space and deep learning, this study proposes a hybrid short-term OWPF framework based on the integrated consideration of spatial and temporal correlations of regional field clusters in the data space and the 3DTCN-CBAM-LSTM model. Firstly, other offshore wind farms with spatio-temporal correlation with the target offshore wind farm are screened out using the K-means spatial clustering algorithm. The historical power sequences determined to be similar to the target offshore wind farm and feature data of target offshore wind farms are used as inputs to the prediction model to improve the efficiency of the framework's power prediction. Secondly, the short-term power prediction framework of offshore wind power based on the 3DTCN-CBAM-LSTM model is constructed, using the 3DTCN model's ability to capture the spatio-temporal dynamic characteristics of offshore wind farm data to improve the accuracy of the prediction model. The temporal characteristics of the data are strengthened using the LSTM model to improve the stability of the prediction model. Finally, the CBAM mechanism is introduced to combine channel attention and spatial attention to eliminate irrelevant information and capture the spatio-temporal characteristics of the data in a more targeted way, which further improves the accuracy of OWPF. To verify the practicality and reliability of the wind power prediction framework described in this study, the differences between the prediction results of the framework and those of the benchmark model are compared using single-step prediction and multi-step prediction (4, 6, 8, and 12 steps). Compared with the benchmark model (BPNN), the single-step prediction results show that the MAE, MAPE, and MSE metrics of the 3DTCN-CBAM-LSTM model are reduced by 20.004 MW, 50.13%, 55.61%, and 80.29%, respectively, and the R2 is improved by 3.39%, which is a substantial improvement in prediction performance. The multi-step prediction results show that, with the progression of the prediction steps, the 3DTCN-CBAM-LSTM model has the highest prediction accuracy and model fit at each step. Therefore, the proposed 3DTCN-CBAM-LSTM model presents obvious advantages and reliability in the prediction of offshore wind power multi-step power.

Deep learning-based evaluation of photovoltaic power generation

Photovoltaic (PV) power generation has emerged as a rapidly growing renewable energy source. However, the PV system output’s intermittent and weather-dependent nature poses challenges when integrating with the power grid. These challenges manifest as critical issues, including voltage fluctuations, harmonic distortion, and current deviation, making it difficult to accurately predict grid conditions. Moreover, the spatial variability caused by PV system intermittency further complicates the situation. To address these challenges and ensure efficient grid integration, this paper proposes a comprehensive approach encompassing deep learning-based state prediction of PV power output. The paper introduces the utilization of a long short-term memory (LSTM) model, a type of deep learning architecture, for learning patterns from historical PV power generation data and weather forecasts. The LSTM model enables accurate predictions for effective grid management by capturing long-term dependencies in PV power generation data. Real-world PV power generation data was employed to evaluate the proposed approach. The results demonstrated the significant improvement in PV power generation prediction accuracy achieved by the LSTM model compared to traditional methods. The proposed approach offers a promising solution for addressing the challenges associated with PV system integration into the power grid. It enables enhanced grid planning, resource allocation, and protection measures to accommodate the increasing penetration of solar energy harnessed through PV systems and related power electronics interfaces.

A Deep Learning Quantile Regression Photovoltaic Power-Forecasting Method under a Priori Knowledge Injection

Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system.

Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data

Due to its strong dependence on weather conditions, photovoltaic (PV) power is highly intermittent in nature. In light of this, this paper introduces a PV power prediction model based on similar-day clustering and temporal convolutional network (TCN) with bidirectional long short-term memory (BiLSTM) model. Firstly, in the data preprocessing stage, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method is used to decompose and denoise the original PV power data to obtain subsequences of different frequencies. Subsequently, the sample entropy (SE) method is incorporated to reconstruct the subsequences and generate low-frequency and high-frequency subsequences with more pronounced temporal features. In addition, a self-organizing map (SOM) with the K-means algorithm is used to categorize historical days into four weather types: sunny, cloudy, rainy, and intermittent. Through grey relational analysis (GRA), meteorological factors significantly affecting PV power prediction are identified to construct a multidimensional feature set. During the model prediction phase, historical PV power data and related meteorological impact factors are input into the TCN-BiLSTM model to perform multi-data-driven PV power prediction. Finally, the predicted results of each subsequence are linearly combined to obtain the ultimate PV power prediction. The proposed model is compared with convolutional neural network (CNN)-LSTM, LSTM, and TCN models, achieving a reduction in the root mean square error (RMSE) of 0.129 kW, 0.238 kW, and 0.257 kW, respectively. This demonstrates that the proposed model exhibits superior overall performance in time series modeling and information capture, enhancing the understanding of seasonal, periodic, and irregular patterns in PV power generation data.

The Representation of Children and Society in Chinese Fifth Generation Cinema: A Test of Semiotics in Film Interpretation

In the exploration of the representation of children and the influence stemming from the dominant ideology in Chinese cinema, I attempt to conduct this research using “semiotics”. The method in question is pretty useful in mirroring the meanings of my chosen stills from the Fifth Generation cinema and is able to set up some linkages with the social environment. The mainstream semiology works in the aspect of single elements and the social semiotics is in operation in the arena of interactive meaning-making among components as a whole. The Syntagmatic comparison keeps track of any slight changes happened to the children, the props serve as the signifiers of interpersonal relationships and power relations, the metaphor between the Tiananmen Square and the class in power acts as a shared code to take into account the external environment. However, semiotics is too dependent on the interpretative practice, and will not be strong enough without the participation of existing theories and Foucaultian analysis to take into account the particular historical discourse and power relations.

Improved informer PV power short-term prediction model based on weather typing and AHA-VMD-MPE

Precise prediction of PV power in the short term is crucial for maintaining power system stability and balance. However, the performance of conventional time series prediction models on short-term long series prediction is scarcely sufficient because of the stochastic and turbulent character of PV power data. This work suggests a PV short-term power forecast model based on weather type, AHA-VMD-MPE decomposition reconstruction, and improved Informer combination to tackle this issue. Firstly, a SUM-ApEn-K-mean++ multidimensional clustering method to group the dataset by weather conditions. Then an AHA-VMD-MPE decomposition model is proposed to decompose the historical power data Finally the Informer model is improved and the improved model is utilized to predict the PV power under various weather conditions. The model exhibits great accuracy and stability in short-term PV power prediction, as demonstrated by the experimental results, which were validated using measured data from many PV power plants.

A Parallel Prediction Model for Photovoltaic Power Using Multi-Level Attention and Similar Day Clustering

Photovoltaic (PV) power generation is significantly impacted by environmental factors that exhibit substantial uncertainty and volatility, posing a critical challenge for accurate PV power prediction in power system management. To address this, a parallel model is proposed for PV short-term prediction utilizing a multi-level attention mechanism. Firstly, gray relation analysis (GRA) and an improved ISODATA algorithm are used to select a dataset of similar days with comparable meteorological characteristics to the forecast day. A transformer encoder layer with multi-head attention is then used to extract long-term dependency features. Concurrently, BiGRU, optimized with a Global Attention network, is used to capture global temporal features. Feature fusion is performed using Cross Attention, calculating attention weights to emphasize significant features and enhancing feature integration. Finally, high-precision predictions are achieved through a fully connected layer. Utilizing historical PV power generation data to predict power output under various weather conditions, the proposed model demonstrates superior performance across all three climate types compared to other models, achieving more reliable predictions.

Seismic Assessment of an Old Historical Unreinforced Masonry Power House Building

Abstract Design of unreinforced masonry buildings is a major concern in seismically active zones in Nepal. The ChandraJyoti Jalvidhyut Power House, a historical landmark in Nepal, represents the nation’s pioneering effort in hydropower generation and signifies its rich cultural heritage. This study comprehensively assesses the structural assessment and seismic performance deficiencies of the ChandraJyoti Jalvidhyut Power House. Material tests on bricks with the individual properties and behaviour of construction materials were done. Utilizing a finite element model in DIANA software V 10.5, the structure’s response under pushover loading conditions is simulated, providing insights into its structural behaviour. Additionally, fragility curves are developed to correlate earthquake intensity with the probability of damage, offering essential information for preservation strategies and seismic risk mitigation. The compressive strength test of bricks, with a value of 9.6 MPa, falls short of the 10 MPa minimum requirement specified by NBC 203:2015, indicating insufficient load-bearing capacity. The building’s pushover analysis shows that the longitudinal direction experiences higher lateral forces (base shear of 945.87 kN) and greater drift ratios exceeding 0.4%, which increases the likelihood of cracks compared to the transverse direction (base shear of 826.77 kN). The analysis also highlights significant nonlinear behavior in both directions, with maximum roof displacements of 35.18 mm (transverse) and 34.4 mm (longitudinal), reflecting the impact of structural irregularities.

Load Forecasting using ANN

Accurate load forecasting plays a key role in economical use of energy. Artificial Neural Network (ANN) models have been extensively implemented to produce accurate results for short-term load forecasting with time lead ranging from an hour to a week. In this report daily peak load forecasting has been performed for the part of a town supplied by 2 distribution feeders on weekdays by taking into consideration the historical maximum Power consumption in MWH, Voltage in KV and Current in Amp data. Optimization of the network parameters is performed for both learning rules. Energy demand forecasting is of great importance in the management of power systems. In this report artificial neural network technique (ANN) is used for forecasting the load curve. Algorithms using these techniques have been programmed using MATLAB 15 and applied to the case study. The efficiency of both the model is determined from the load curve and the load is predicted as a testing sample. The ANN model trains the daily load data for a set of days and then forecast the load for next day. Actual data are obtained from "Mankapur Substation" is used to validate the result. Keywords: Load forecasting; Load demand; Power Quality; MATLAB; Load

Ultra-short-term wind power forecasting based on a dual-channel deep learning model with improved coot optimization algorithm

The large-scale integration of wind power to the grid poses some potential challenges to the power system. Accurate wind power forecasts reduce the impact of the nonlinearities and volatility of wind power generation. A two-channel deep learning model based on an improved coot optimization algorithm (ICOA) is proposed. First, the use of long short-term memory (LSTM) builds a channel for the extraction of chronological characteristics of historical power. Second, a temporal convolutional network (TCN) is adopted as a hybrid feature-extraction channel for multi-dimensional meteorological data, and by incorporating the self-attention mechanism (SA), the ability of TCN to extract internal information from meteorological features is enhanced. Finally, the ICOA that introduces nonlinear decision factor, adaptive dynamic boundary, and Cauchy mutation is used to optimize the model hyperparameters. The simulation analysis is carried out on the winter and summer measured data of a wind farm in Xinjiang. The results show that compared with the traditional LSTM model, the root mean square error and the mean absolute error of the proposed method are reduced by 10.35 % and 16.27 % on average, respectively, and the prediction accuracy is higher than that of other comparative models, which verifies the superiority of our proposed model.