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Electricity load & price forecasting Features

  • Universal AI forecasting platform for price, load & renewable generation.
  • Deep Learning & Machine Learning algorithms for energy forecasting.
  • Automated feature engineering, model optimization and backtesting modules.
  • No coding auto-ML solution.

QR AI Forecaster is a universal AI energy forecasting platform designed for all the needs of electricity markets. It offers integrated Data Processing and AI machines.

QR Data Processor & Feature Extractor

Common Features

  • QR AI Forecaster can be used for all your forecast needs in electricity markets: load, energy and ancillary prices, offer stack, and solar generation. Libraries of premade models are available.
  • QR AI Forecaster is a no-coding AI platform. Users can drag-and-drop and combine various Deep Learning and Machine Learning methods, in a user-friendly dashboard, to create custom forecast models.
  • Analytics Governance is built in: each AI model and its parameters are visible and auditable. Each execution of an AI run is sand-boxed and parameters, input data and output results are saved individually.
  • The data frame of predictors, and the parameters of each AI model are configured on the screen in a user-friendly dashboard, without scripting or coding. No prior knowledge of AI, statistics and programming are needed to use QR AI Forecaster platform.
  • Each forecast can have upper and lower uncertainty bands computed via quantiles or standard deviation.
  • When forecasting load or price, many other time series can be added to the AI model as predictors. These can be multiple weather indicators, demand and supply side data, generation by fuel type including renewables, outages, ISO published hour and day ahead forecasts, consumer load or SCADA. Our AI models make a judicious use of predictors depending on the forecast type. Post-training, the system ranks the predictors by relevance, to help optimize the model.
  • A different calendar can be assigned to each AI forecast model to replace intra-week holidays’ data with nearest Sunday’s data, both for training and prediction.
  • You can create AI forecasting models for short term, intraday, up to 7 day ahead, and long term forecast. Forecast resolutions are 5 to 60 minutes. The frequency of short term forecast and publication is every 10 minutes, 24/7.
  • You can set the date range for model training, and the forecast horizon.

Feature Extraction

  • QR AI Solar Generation Forecaster is a purely data driven AI solution which uses granular solar generation data as the forecast target, and weather and radiance data as predictors.
  • The data analytics and feature extraction module builds and extracts deep features for AI modeling from optimal combinations of various weather and radiance indicators, DNI (Diffused Normal Irradiance), DHI (Diffused Horizontal Irradiance) and GHI (Global Horizontal Irradiance).
  • We can license weather and radiance data from a range of vendors, and/or your can provide your own.
  • To achieve greater accuracy, we forecast solar generation per single farm with pinpoint precision radiance data. We can then aggregate these forecasts across larger portfolios of solar assets.
  • Our data science team will closely work with you to configure the optimal AI model for your solar assets.
Electricity Load AI Forecasting and Price AI Forecasting Dashboard
  • Due to the uncertainty of weather and radiance data QR AI Solar Generation Forecaster can also produce probabilistic solar generation forecasts with average, high and low forecasts at any desired percentile, e.g., 5 and 95, 10 and 90, 15 and 85, etc. For example, the 5 or 95 percentiles can be used to estimate lower generation scenarios for risk management in planning and hedging.
QR AI Forecaster has a built-in data processing service which automates complex data science tasks by offering a range of seamlessly integrated data processing and feature and predictor extraction libraries especially designed for power markets. These lighten the workload of data scientists tremendously, and provide data and model integrity and governance.

Data Processing

  • This no-coding platform allows users to drag and drop various data (time series), apply the desired mathematical operations and prepare and aggregate the outputs in a “Data Frame” to be used as the predictor input for the AI model.
  • Multiple time series with different timescales can be used (e.g., weather can be hourly and prices 15 minutes). The Data Processor will load data with the finest timescale and use empty fillers for missing data, awaiting the selected gap-filling method for each data type.
  • To enact data processing, you can drag and drop from the menu a mathematical method to apply to a predictor. These are simple functions (e.g., logarithm, exponential, averages, etc.), gap-filling, outlier detection and replacement, scaling, quantization, encoding methods. The parameters of each method appear in a popup window for editing upon clicking on the method icon.
  • You can set the date range for model training, and the forecast horizon.

AI Models

QR AI Forcaster Dashboard

  • QR AI Forecaster offers a comprehensive set of Deep Learning and Machine Learning methods powered by Python libraries such as “TensorFlow”, “Keras”, “Scikit-learn”. These are preconfigured for electricity load and price data to perform regression for forecasting, or classification (used e.g., in spike predictions, or DA reserve pricing).
  • No coding is required, the models and their parameters are accessible in an intuitive web dashboard.

AI & Machine Learning Models

  • Deep Learning: QR AI Forecaster offers a modular deep learning layer architecture, allowing users to add/remove different types of deep layers via drag-and-drop in an intuitive web-dashboard. Users can then design a wide range of deep models by connecting the layers sequentially or in parallel. QR deep models support multiple data frames as input. The following deep models from Keras library are available: Long Short Term Memory Networks (LSTM), 1D/2D Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Gated Recurrent Unit (GRU).
  • Ensemble Models: QR AI Forecaster offers the following set of ensemble tree-based machine learning models. These distributed gradient boosting libraries are highly efficient, flexible, and portable. The models used are: XGBoost, Random Forest, Catboost, ADABoost and LightGBM.
  • Single Models: QR AI Forecaster offers some classical models, e.g., decision tree, SVM, KNN, ElasticNet, MLP (neural networks), and Huber Regressor.

AI Model Optimization

Model Splitting

  • Electricity data has clear daily profiles e.g., peak, off, super peak, and similarly intra-term blocks such as weekdays and weekend, this is in total 3 x 2 or 6 profiles. QR AI machine allows defining splitters in custom blocks or profiles. Each block is automatically assigned to its own AI model, and its training data is obtained by splitting the main time series at run-time according to the selected profiles.
  • Model splitting amounts to the automatic creation of a series of AI models that are more robust and accurate since they are adapted to different regimes of data.

Train & Export AI Models

QR AI platform allows you to train an AI model on schedule, e.g., every night at 1.00 am, and save the trained model for execution the rest of the day, e.g., every 15 minutes, while using the latest updated predictors as new data arrives.

Features & Predictors Importance Analysis

  • Identifying the optimal set of predictors or features is key in optimizing AI models. During training, QR AI Forecaster performs an analysis of the predictors and ranks them by importance or effectiveness, and displays the results in a bar chart plot.
  • Users can iteratively add predictors, retrain the AI model and retain the best model.
Electricity AI Load Forecasting and AI Price Forecasting Feature Importance Dashboard

Data Dashboard

  • Since bad input data negatively impact an AI model, it is crucial to be able to inspect the data of each AI model, namely the target, predictors and forecast data, before and after every stage of data processing.
  • All above data are gathered with plots in different sections of the dashboards for quick and easy visualization and inspection by data scientists, saving them the efforts to write code or run scripts.
Sample Load Forecasting Dashboard Displaying Actual & Forecast Load

QR AI Model Fine-Tuner

AI model optimization is a key step in ensuring the accuracy and the stability of forecasts. AI models can have dozens of parameters, and identifying the optimal combination that produces the best result (in terms of accuracy, shape, and behavior of forecasts) is very time-consuming and difficult. This toolbox automates the complex task of identifying the optimal set of parameters by bootstrapping a genetic computing process around a limited number of forecasts. The optimal set of parameters are those that minimize the forecast error (MAEP) over a date range. This algorithm replaces the equivalent of hundred thousand manual trial and errors. This tool operates in two different modes, back-test and forward-test.
  • In back-test mode, the fine-tuner finds the best set of parameters over a past date range, e.g., one or more days.
  • To further improve the results of back-testing, the forward-test mode takes the optimal back-test parameters and estimates the best set of parameters that could have predicted the actual data that has become available over a range of dates.

Forecast Accuracy Analysis

  • We offer powerful and flexible no-coding tools to create reports and dashboards, and to visualize data.
  • Reports and dashboards can combine multiple data sources, e.g., actual raw data and forecast data, leading to accuracy analysis with any desired error indicator, e.g., MAPE, SMAPE, MAE.
  • Dashboards can be created to display side by side the errors of multiple forecasts jobs with different warning thresholds for error reporting, e.g., green for good to red for high error.
Forecast View Samples

Load, Price & Renewable Generation Forecasts Brochures & Case Studies

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Let's talk about your needs

We look forward to exploring the range of options for your projects. Please write to us and one of our project managers will get back to you at once.