English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The modern power grid is facing increasing complexities, primarily stemmingfrom the integration of renewable energy sources and evolving consumptionpatterns. This paper introduces an innovative methodology that harnessesConvolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networksto establish robust time series forecasting models for grid frequency. Thesemodels effectively capture the spatiotemporal intricacies inherent in gridfrequency data, significantly enhancing prediction accuracy and bolsteringpower grid reliability. The research explores the potential and development ofindividualized Convolutional LSTM (ConvLSTM) models for buildings within auniversity campus, enabling them to be independently trained and evaluated foreach building. Individual ConvLSTM models are trained on power consumption datafor each campus building and forecast the grid frequency based on historicaltrends. The results convincingly demonstrate the superiority of the proposedmodels over traditional forecasting techniques, as evidenced by performancemetrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), and MeanAbsolute Percentage Error (MAPE). Additionally, an Ensemble Model is formulatedto aggregate insights from the building-specific models, deliveringcomprehensive forecasts for the entire campus. This approach ensures theprivacy and security of power consumption data specific to each building.

Grid Frequency Forecasting in University Campuses using Convolutional LSTM