A renewable energy generator running a 1.2 GW portfolio was paying penalties every single day. Its day-ahead generation schedules were wrong by an average of 14.3 percent. Under the Deviation Settlement Mechanism, that error translated into steep financial charges. This is the operational reality that brought us in, and the reason this AI energy forecasting solution exists.
The generator operated wind and solar assets across more than forty sites. Its forecasting team relied on statistical models that could not read weather nonlinearity. Ramp events, cloud cover shifts, and wind wake effects slipped past them constantly. Every miss became a settlement charge, and the charges were compounding.
KriraAI is an AI systems company that builds and delivers production-grade machine learning for enterprise clients. We were engaged to replace a fragile forecasting process with a hardened platform. This blog walks through the full engagement from first session to go live. It covers the broken workflow, the architecture we shipped, the delivery journey, and the measured results the client achieved.
Before our engagement, the client's forecasting desk ran on spreadsheets and legacy statistical tools. Day-ahead forecasts were built using ARIMA-style models and manual weather judgment. These models assumed linear relationships that solar and wind generation simply do not follow. The result was a persistent day-ahead error rate above 14 percent.
That error had a direct price attached to it. Under India's Deviation Settlement Mechanism, generators pay charges when actual output drifts from the scheduled output. Every forecasting miss pushed the portfolio into a penalty slab. Across a 1.2 GW fleet, these charges accumulated into a serious annual cost.
The data to do better already existed, but it was going unused. SCADA systems streamed telemetry from every turbine and inverter at one-minute resolution. Numerical weather prediction feeds arrived several times a day. Satellite imagery captured cloud movement across every solar site, yet none of it was fused into a single forecasting view. We applied similar production AI techniques in another engagement where our AI solutions for solar energy reduced downtime by 34% across a utility-scale solar fleet.
The forecasting team was making critical calls under severe time pressure. Intraday revisions had to be submitted within tight scheduling windows. Analysts manually adjusted numbers based on gut feel and a glance at the weather. By the time a revision was ready, the ramp event had often already happened.
This manual loop could not scale with the portfolio. Each new site added more telemetry, more weather zones, and more complexity. The team was drowning in data they had no way to process fast enough. Forecast quality varied by analyst, by shift, and by fatigue.
Every rupee lost to penalties was a rupee competitors were not losing. Better forecasting directly protects generation margin in a tight power market. The client's leadership understood that inaccuracy was now a strategic liability. They needed forecast error driven down and held down permanently, not just for one quarter.
Curtailment added another layer of cost on top of settlement charges. When schedules were unreliable, grid operators trusted the plant less. That erosion of trust carried real commercial consequences over time. The status quo was quietly bleeding value from an otherwise healthy asset base.
KriraAI designed and delivered a production forecasting and dispatch optimisation platform. The system ingests weather, satellite, and sensor data continuously. It produces probabilistic generation forecasts across multiple horizons. It then recommends intraday schedule revisions that minimise settlement penalty exposure.
At the core sits a Temporal Fusion Transformer, an attention-based architecture for multi-horizon time series. We chose it because it handles static metadata, known future inputs, and observed history together. The model produces quantile forecasts rather than single-point estimates. That means the client sees a full uncertainty band, not just one number. Advanced forecasting platforms like this rely on custom Machine Learning Development Services that combine deep learning, feature engineering, and production MLOps.
Data enters through three parallel paths that converge in a feature store. SCADA telemetry streams in real time from every asset. Weather prediction files arrive in scheduled batches from multiple providers. Satellite frames feed the solar nowcasting model on a rolling basis.
Every input is normalised, time-aligned, and turned into features at ingestion. The feature store serves these features to both training and live inference. This design removes the training and serving skew that breaks many forecasting systems. The same feature logic runs offline and online. Integrating operational telemetry, weather feeds, and satellite imagery requires a modern Data Analytics Solution capable of transforming high-volume industrial data into business intelligence.
Forecasts do not sit in a dashboard waiting to be noticed. The platform pushes finalised schedules directly into the client's energy management system. It also submits schedules to the regional load dispatch portal through a controlled integration. When a ramp risk crosses a threshold, the system alerts the desk immediately.
An optimisation engine sits on top of the forecasts for intraday decisions. It weighs forecast uncertainty against penalty slabs and revision timing. The engine recommends whether to revise a schedule and by how much. This turned a manual guessing exercise into a calculated financial decision.
The platform replaced the old spreadsheet workflow entirely. It augmented the forecasting team rather than removing them. Analysts now supervise a system that does the heavy computation. Their judgement is applied to edge cases, not to every routine forecast.
The AI energy forecasting solution runs as six connected layers. Each layer was designed for a specific job and a specific failure mode. Together they form a system that survives real production load. The following breakdown walks through every layer and the reasoning behind it.
The pipeline handles three ingestion patterns because the sources behave differently. SCADA telemetry arrives as an event stream over MQTT into Apache Kafka. We stream roughly fifty thousand tags at one-minute resolution across the fleet. Asset metadata is captured through change data capture using Debezium.
Numerical weather prediction data arrives as scheduled GRIB batch files. Satellite imagery is pulled on a rolling window for the solar nowcasting model. Apache Flink handles stream processing, windowed aggregation, and feature computation. Apache Airflow orchestrates the batch DAGs that run forecasting jobs on schedule.
Transformation happens at ingestion rather than at query time. We apply schema normalisation, entity resolution across asset identifiers, and temporal feature engineering. A Feast feature store maintains an online path in Redis and an offline path in object storage. This split gives low-latency serving and reproducible training from the same definitions.
The core is built in PyTorch using the PyTorch Forecasting library. Our temporal fusion transformer forecasting core produces quantile outputs across day-ahead and intraday horizons. We train it alongside LightGBM models and fuse the outputs through a learned ensemble. A graph neural network models spatial dependencies and wind wake effects between nearby turbines.
Training runs on a GPU cluster using distributed data parallel execution. Ray manages hyperparameter search across the model family. For serving, we quantise models and deploy them behind Triton Inference Server. This keeps inference latency at the ninety-fifth percentile under two hundred milliseconds.
The integration layer connects AI output to the systems that act on it. External access runs through versioned REST and GraphQL API contracts. Internal services communicate over gRPC for low latency. Kafka topics carry events between the forecasting core and downstream consumers.
Finalised schedules reach the client's energy management system through webhook triggers. Schedule submission to the dispatch portal runs through a controlled and audited adapter. This event-driven design means no component blocks another while waiting. Each part of the system fails and recovers independently.
We track data drift on every weather and telemetry feature continuously. Population stability index and KL divergence flag distribution shift before it hurts accuracy. Model performance is scored against held-out sets using MAPE and pinball loss. Latency is tracked at the fiftieth, ninety-fifth, and ninety-ninth percentiles.
Prometheus and Grafana carry the operational metrics and dashboards. MLflow serves as the experiment tracker and the model registry. Evidently powers the drift reports the MLOps team reviews. When rolling forecast error crosses a defined threshold, an automated retraining job is triggered.
Security was designed in from the first architecture session. The full deployment runs inside a private VPC with no public endpoints. Access follows role-based control with attribute-level data masking. Model inputs and outputs are encrypted in transit and at rest.
Every schedule submission and model decision writes to an immutable, append-only audit store. This satisfies the traceability that energy sector governance demands. The platform aligns with CERC deviation settlement rules and CEA cyber security guidance. Data residency requirements are respected across the entire stack.
The delivery layer is a React-based operations dashboard. It shows forecasts with full quantile uncertainty bands, not flat numbers. Analysts can run scenario comparisons and inspect the drivers behind each forecast. Ramp alerts surface directly in the interface and through messaging channels.
The dashboard also exposes the optimisation engine's revision recommendations. An analyst can accept, adjust, or reject a recommended schedule change. Every action is logged for later review and model feedback. The interface was built so a single desk analyst could run the whole portfolio.
Every technology in this stack was chosen against the client's real constraints. We did not pick tools for novelty. We picked them for scale, reliability, and fit with the existing environment. The rationale below explains each major choice.
We selected Apache Kafka for streaming because the fleet produces high-volume telemetry with strict ordering needs. Kafka gave durable, replayable event logs that a lighter queue could not. Apache Flink handled stateful stream processing better than micro-batch alternatives for our one-minute cadence. This mattered because ramp detection depends on fresh windowed features.
For the forecasting model, we chose the Temporal Fusion Transformer over LSTM and classical methods. It handles known future inputs like scheduled weather, which recurrent models struggle to use cleanly. Its native quantile output gave the uncertainty bands the penalty optimisation needed. LightGBM joined the ensemble because it is fast, strong on tabular weather features, and cheap to retrain.
We deployed serving on Triton Inference Server with quantised models for latency and cost. Feast was chosen as the feature store to kill training and serving skew. Redis backed the online path because sub-millisecond feature reads were required. Object storage backed the offline path for reproducible historical training.
Orchestration used Apache Airflow because the client already ran it elsewhere. This reduced operational learning cost for their platform team. For optimisation, we used a mathematical solver to model the penalty structure precisely. The full platform ran on the client's existing cloud footprint to respect data residency.
The engagement ran across seven phases from first session to go live. We work in tight delivery loops with the client's own engineers embedded. This is how KriraAI keeps enterprise AI projects grounded in operational reality. Below is the honest journey, including the problems we hit.
The first four weeks were a full data and workflow audit. We mapped every telemetry source, weather feed, and manual step. This is where we found the first hard problem. Around eight percent of SCADA tags had gaps or timestamp misalignment.
We spent the next weeks designing the six-layer architecture and building the pipeline. Timezone and timestamp inconsistency across weather sources caused early failures. We built a validation and imputation stage to clean streams before they reached features. Only then did the feature store produce trustworthy inputs.
Model development ran for roughly eight weeks and did not go smoothly at first. Our initial models plateaued at a day-ahead error near nine percent. Solar ramp events during cloud transitions were the biggest source of miss. We added the satellite nowcasting network, which pushed accuracy meaningfully lower.
Wind sites brought a second modelling challenge. Turbines in a cluster affect each other through wake effects. Independent site models could not capture this coupling. We introduced a graph neural network to model the spatial dependencies directly.
Integration testing surfaced a rework problem with the legacy energy management system. Its expected schedule format did not match our first adapter design. We rebuilt the adapter around a versioned contract and added strict validation. This prevented malformed schedules from ever reaching the dispatch portal.
Before go-live, we ran the platform in shadow mode for four weeks. It produced forecasts in parallel with the existing process without acting. This let the client compare accuracy day by day with zero operational risk. The shadow numbers gave leadership the confidence to switch over fully.
Handover included documentation, runbooks, and training for the client's team. We did not walk away at go-live. KriraAI ran a supervised stabilisation period while the team took ownership. This delivery discipline is how we make sure a system survives after we step back.
The results were measured over the first six months after go-live. Day-ahead forecast error fell from 14.3 percent to 6.1 percent. Intraday forecast error settled below 5 percent on most days. These accuracy gains flowed straight into financial outcomes.
The optimisation engine was built to reduce deviation settlement penalties directly. Deviation Settlement Mechanism charges dropped by around 60 percent over the same period. The engine caught penalty exposure early and recommended timely revisions. The forecasting desk stopped guessing and started deciding on numbers.
Speed changed as dramatically as accuracy. Generating a full portfolio forecast once took more than three hours of manual work. The platform now produces the same forecast in under four minutes. That freed the team to focus on exceptions rather than routine computation.
The predictive maintenance capability added a second stream of value. Anomaly detection on SCADA flagged three gearbox issues two to three weeks early. Each early warning avoided an unplanned outage and its lost generation. These were failures the old reactive process would have missed.
Platform reliability held throughout the measurement window. Inference latency stayed under two hundred milliseconds at the ninety-fifth percentile. System availability held at 99.9 percent across the six months. The client moved from firefighting to running a stable, measurable operation.
The platform was built to grow without being rebuilt. As the client adds new sites, the pipeline absorbs the extra telemetry automatically. The feature store and model family scale horizontally with the fleet. No architectural rewrite is needed when the portfolio doubles.
New use cases sit naturally on the same foundation. The client is already extending the platform toward battery storage optimisation. Because the data and serving layers are shared, this reuses most of the existing stack. Price forecasting and energy trading signals are the next planned additions.
The roadmap over the next two to three years is an evolution, not a replacement. The client plans automated bidding informed by the same probabilistic forecasts. Reinforcement learning agents will take a larger role in revision decisions over time. Each addition compounds on the infrastructure KriraAI already delivered.
Other energy companies can apply the core pattern directly. This renewable energy forecasting AI pattern, fusing weather, satellite, and sensor data, is broadly reusable. So is the discipline of designing for drift, uncertainty, and audit from day one. The specific models change by asset mix, but the architectural spine holds across the sector.
The lesson for peers is that forecast accuracy is an engineering problem, not a licensing one. A generic tool bought off the shelf rarely fits a specific asset portfolio. A designed system that respects local physics and regulation performs far better. That is the difference we saw reflected in the settlement numbers.
This engagement left three insights worth carrying forward. The technical insight is that fusing weather, satellite, and sensor data beats any single source. Probabilistic forecasting with a Temporal Fusion Transformer outperformed the client's statistical baseline decisively. Modelling uncertainty, not just the mean, is what made the penalty optimisation possible.
The operational insight is that speed and accuracy reinforce each other. A forecast that takes four minutes instead of three hours changes how a desk works. The team moved from routine computation to managing exceptions and edge cases. That shift is where much of the sustained value came from.
The strategic insight is that forecast accuracy is now a competitive asset in the energy sector. Every point of error carried a settlement cost and eroded grid trust. Driving error down and holding it down protected real margin. An AI energy forecasting solution built for the specific portfolio delivered what generic tools could not.
KriraAI builds production-grade AI systems that survive real enterprise workloads, not demos. We bring principal engineer depth to architecture and disciplined delivery to every phase. This same rigour goes into every client engagement we take on, across the energy sector and beyond. If generic tools have failed to solve your AI challenge, bring it to KriraAI. Let us build the system your operation actually needs.
An AI energy forecasting solution improves accuracy by learning nonlinear patterns that statistical models cannot capture. Ours uses a Temporal Fusion Transformer to combine weather prediction, satellite imagery, and live sensor data. It produces probabilistic forecasts across multiple horizons rather than single point guesses. For this client, day-ahead error fell from 14.3 percent to 6.1 percent within six months. The gain comes from fusing many data sources and modelling uncertainty directly. Renewable energy forecasting AI works because it reads ramp events and cloud dynamics that manual methods routinely miss.
Energy generation forecasting today relies on transformer-based time series models combined with gradient boosting and physics constraints. We used temporal fusion transformer forecasting as the core, ensembled with LightGBM on engineered weather features. A convolutional network handled satellite-based solar nowcasting for short horizons. A graph neural network modelled wind wake effects between nearby turbines. This combination captures learned patterns and known physical behaviour together. Single model approaches underperform because no one architecture handles weather, physics, and spatial coupling well on its own.
AI can reduce deviation settlement penalties substantially when forecast error is driven down, and revisions are optimised. For this renewable generator, penalties dropped by around 60 percent within six months of go-live. The reduction came from two things working together. First, day-ahead accuracy improved sharply from 14.3 percent error to 6.1 percent. Second, an optimisation engine recommended timely intraday revisions based on penalty slabs. Forecast error is the direct lever, because charges scale with the gap between scheduled and actual generation.
A production AI energy forecasting solution typically takes several months to implement properly, not weeks. Our engagement ran across seven phases, including a four-week discovery audit and roughly eight weeks of model development. We also ran a four-week shadow deployment before go-live to prove accuracy safely. AI implementation for energy utilities depends heavily on data quality, integration complexity, and regulatory requirements. Rushing deployment without a shadow period and clean data pipelines is the most common cause of failed energy AI projects.
AI energy forecasting can be fully secure and compliant when security is designed in from the start. Our platform runs inside a private VPC with no public endpoints and encrypted data flows. Access uses role-based control with attribute-level masking, and every decision writes to an immutable audit log. The system aligns with CERC deviation settlement rules and CEA cyber security guidance. Compliance and data residency are not optional extras. AI implementation for energy utilities must treat governance as a core layer, not an afterthought.
Founder & CEO
Divyang Mandani is the CEO of KriraAI, driving innovative AI and IT solutions with a focus on transformative technology, ethical AI, and impactful digital strategies for businesses worldwide.
