Shaping the greener energy economy for the coming decades

Our Solutions

Predictive analytics is helping to get most from the available renewable sources and energy sector players are adopting the next technological frontier to remain competitive in this sector. StartLytics has in-depth experience in building machine learning models for predicting commercial and domestic energy consumption and for preventive maintenance of IoT devices.

Demand Forecasting

  • Day-ahead Forecasting
  • Long-term Forecasting

Financial Forecasting

  • Revenue Forecasting
  • Expense Forecasting

Preventive Maintenance (IoT)

  • Transformers
  • Feeders
  • Water Pipeline
  • Gas Pipeline

Capex Planning

  • Network Analysis
  • Ageing Analysis

Regulatory Search Engine

  • RPA – Data Extraction
  • NLP of Legal Documents


  • Customer Segmentation
  • Tariff planning and recommendation
  • Demand Response Management
  • Chatbots for customer service

Case Studies

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10% Reduction in breakdown of Transformers

We collect data from IoT sensors installed in each distribution transformer and enrich it with past maintenance history to accurately predict high risk transformers that are likely to fail. Powered with this information, proactive maintenance of transformers can be done to reduce down-time by at least 10%.

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Reduce your ABT/DSM penalty by 15% with better Day-ahead load forecast

With use of machine learning algorithm, we have improved forecast accuracy over traditional methods for Distribution companies. This has helped improve grid stability and reduce penalty by at least 15% as per prevailing ABT/DSM regulations.

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Prepare your Annual Revenue Requirements (ARR)

We help Discoms prepare ARR using historical data to estimate future expenses for each category (Repair & Maintenace, Operations, Employees cost, Fixed cost etc).

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Planning Consumers’ Electricity Demand

While predicting electricity at a distribution level is easier because of the aggregated nature of the data, predicting at a customer level is difficult because of the additional factors like consumer usage pattern, local weather, power generated from rooftop solar panels, storage capacity, etc. The solution was created by collecting both internal and external data though various API endpoints and the final ML model was integrated into the client’s architecture using AWS Lamba capabilities. This solution achieved an overall accuracy of demand prediction is >95%.