IEMS offers an AI-based predictive analysis solution that can help energy companies, VPPs and utilities optimize their operations and make data-driven decisions. The solution provides advanced machine learning algorithms for data preprocessing, feature engineering, and model training, enabling the creation of accurate predictive models that can forecast energy demand, pricing, and supply. The system also offers real-time monitoring and alerts for anomaly detection, allowing stakeholders to respond quickly to issues that may affect operations. With IEMS’s predictive analysis solution, VPPs, energy companies and utilities can improve their operational efficiency, reduce costs, and increase customer satisfaction by delivering reliable and uninterrupted energy services.

Through the IEMS AI-based Predictive Analytics we offer:

  • Predictive Analytics SaaS
  • Option to select location to fetch meteorological/irradiance data automatically
  • Option to select analytical/data driven forecast models
  • Providing Probabilistic (Stochastic) Forecasts
  • Support several data import/export options
  • Short Term Load Forecast: 5-minute to 24-hours Electric Load prediction (STLF)
  • Short Term Photovoltaic Forecast: 5-minute to 24-hours Solar PV generation prediction¬† (STPF)
  • 5-minute to 24-hours EV-fleet demand prediction
  • 5-minute to 24-hours Wind generation prediction
  • Short Term LMP or DLMP Forecasting
  • Interactive Data Visualization

The most important features for the IEMS AI-based predictive analysis are:

  • Data collection: Collecting relevant data from various sources, including historical data, real-time data, and external data sources.
  • Data preprocessing: Preprocessing and cleaning data to ensure that it is accurate, complete, and ready for analysis.
  • Feature engineering: Extracting relevant features from the data that can be used to build predictive models.
  • Machine learning algorithms: Implementing machine learning algorithms such deep neural networks, and other advanced techniques to build predictive models.
  • Model training and validation: Training and validating the predictive models to ensure that they are accurate and reliable.
  • Performance metrics: Measuring the performance of the predictive models using relevant metrics such as accuracy, precision, recall, and others.
  • Model deployment: Deploying predictive models into production systems and integrating them into business workflows.
  • Monitoring and maintenance: Monitoring the performance of the predictive models in real-time and performing regular maintenance to ensure that they remain accurate and up-to-date.


Communication Interfaces:

  • DNP3
  • OpenADR
  • IEEE2030.5
  • OCPP
  • ISO 15118
  • Restful API
  • SFTP

Data Format Compatibility:

  • Common Information Model (CIM) Import/Export

Cybersecurity Features:

  • Secure web portal with multifactor authentication (MFA)
  • Encryption for data at rest and transition
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