Get Sharper AI Forecasts From Top Companies Providing Energy Data Engineering Service

AI forecasting is becoming a bigger priority for energy companies, but the quality of a forecast still depends on the quality of the data behind it. Itron’s 2025 Resourcefulness Report surveyed 500 electric utility executives in the US and Canada, and 51% named demand forecasting a top-three priority for AI and grid-edge analytics. Read Itron’s Resourcefulness Report.

For energy teams, that creates a practical question before model development begins: do you have the right data, at the right level of quality, for the forecast you want to build? Meter history, weather data, outages, tariffs, asset conditions, and other operational signals each affect different forecasting use cases, and gaps in those inputs can limit what even a strong model can deliver.

This guide helps you work through that preparation. You will see which data inputs different energy forecasts require, what engineering work makes those inputs usable, and where to focus first if your current data is incomplete or fragmented.

You will find 7 top companies providing energy data engineering services and a final section on how to keep human review and operational judgment part of the forecasting process.

Key Takeaways

• AI energy forecasts depend on six data types: meter and generation history, weather, calendar and tariffs, asset and network events, market prices, and trusted actuals.
• Engineering work aligns timestamps, handles gaps, joins weather to each site, versions training sets, and watches for drift.
• People stay in charge, as forecasters, operators, data owners, and analysts review, override, approve, and back-test the output.
• A vendor that explains these steps with examples from data like yours shows real forecasting experience.
• The best companies providing energy data engineering services include Relevant Software, SumatoSoft, Sombra, Sii Poland, Simform, Integra Sources, and Endava.

The Gap Between Energy AI Plans and Data Readiness

Several industry findings show why energy companies need to focus on data readiness before investing heavily in forecasting models.

• About 60% of European households had an electricity smart meter at the end of 2024, and 15 EU countries passed 80%, according to ACER data cited by the European Commission. Forecasters now have access to interval history at scale. European Commission: Smart Grids and Smart Meters.
• In DNV’s Energy Industry Insights 2024, 74% of more than 1,300 energy professionals said their organizations would prioritize better data quality and availability in the year ahead. Data readiness now shapes AI plans. DNV Energy Industry Insights 2024.
• Black & Veatch’s 2025 Electric Report surveyed 500 US energy stakeholders, and only 19% expressed strong confidence in forecasting the increase in power loads. Planners need stronger inputs and models to close that confidence gap.

These findings point to a common challenge: energy companies are collecting more information than ever, but turning that information into reliable forecasting inputs requires careful engineering.

The Data Inputs Behind Reliable Energy Forecasts

Before you evaluate a forecasting model, check whether the underlying dataset captures the conditions that actually move demand, generation, or price.

Use the six data types below as a checklist when you prepare or review a forecasting dataset. Together, they give the model the historical patterns, external conditions, operational events, and ground truth it needs to learn from the right signals.

1. Meter and Generation History

Interval reads from meters and output records from plants form the base of every model. History that spans multiple seasons lets the model learn weekday patterns, seasonal swings, and growth trends.

Gaps and estimated values need flags so your model can tell real readings from filled ones.

2. Weather Observations and Forecasts

Temperature drives demand, while irradiance, cloud cover, and wind speed drive solar and wind output.

Models need past observations for training and forecast weather for prediction, matched to the location of each site. Weather from the wrong station can undo the rest of your data work.

3. Calendar and Tariff Data

Holidays, school terms, daylight saving shifts, and tariff changes all move consumption.

A calendar table that marks these events lets the model separate a holiday dip from a real drop in demand. Your pricing team often holds the tariff history, so bring them in early.

4. Asset and Network Events

Outages, maintenance windows, curtailment orders, and new connections change what a site can produce or consume.

Without these records, the model treats a planned shutdown as a pattern to repeat. Event logs from your asset and outage systems keep those one-off hours out of the forecast baseline.

5. Market Prices and Signals

Day-ahead and intraday prices, imbalance costs, and demand response calls matter for price forecasts and for any load that reacts to price.

These feeds come with their own time zones and settlement rules. Alignment with your operational data prevents silent offsets of an hour or more.

6. Labels and Ground Truth

Every forecast needs a trusted actual value to compare against, such as settled meter data or metered plant output.

Agree on which source counts as the truth before training starts. That choice decides how your team measures accuracy later.

Energy Data Engineering Steps That Make Forecast Inputs Usable

Having the right data sources is only the first step. Before those inputs can support a forecasting model, they need to be aligned, cleaned, joined, versioned, and monitored in a way that keeps the training data consistent over time.

The engineering practices below show where to focus that work. They help your team turn raw operational feeds into datasets that are easier to train on, trace, update, and trust when the model moves into production.

• Time alignment across your sources: Pipelines convert every feed to one time standard and one interval length. Weather, meter, and market data then line up hour by hour.
• Gap handling you can audit: Validation fills missing intervals by set rules and flags each estimate. Models can then weigh real readings more heavily than filled ones.
• Site-level weather joins for your assets: Each meter or plant links to the nearest weather point or a gridded forecast. Local conditions then reach the model for every site.
• Versioned training sets for your models: Each dataset carries a version and a record of its sources. Any forecast then traces back to the exact data behind it.
• Drift monitoring on your live inputs: Checks watch for shifts in data patterns, such as a new meter type or a changed feed. Your team hears about the change before accuracy drops.

These practices are also relevant to other data-intensive AI applications. TechPluto previously explored the power of AI in collecting real-time data, including how better access to large datasets can support business intelligence and decision-making.

Side-by-Side Look at Top Companies Providing Energy Data Engineering Services

The table sets seven companies side by side, with a column for the energy data each one already works with. Clutch scores, hourly rates, and one proof point from each firm’s published work fill the other columns.

Company	Clutch Rating	Hourly Rate	Energy Data They Work With	Proof Point
Relevant Software	4.9 (32 reviews)	$50–$99	Sensor, IoT, SCADA, ETRM, and weather data	Solar forecasting tool: +20% accuracy
SumatoSoft	4.8 (25 reviews)	$50–$99	Wind turbine SCADA and IoT sensor data	Wind farm failure prediction: −38% unplanned downtime
Sombra	4.9 (46 reviews)	$50–$99	SCADA, AMI, EMS, weather, and pricing data	AI inspection review for an LPG distributor: up to −80% manual review time
Sii Poland	4.9 (44 reviews)	Not listed	Sensor, device, weather, and SCADA data	Not listed
Simform	4.8 (88 reviews)	$25–$49	EV charger and battery telemetry	Charging platform for 100,000+ vehicles charged
Integra Sources	4.9 (26 reviews)	$50–$99	Home solar and battery storage data	Solar monitoring cloud with 25,000 concurrent requests
Endava	Not listed	Not listed	Demand, battery, and meter data	Peak demand forecasting: −60% electricity cost for end customers

1. Relevant Software

Relevant Software provides energy data engineering services for utilities, renewable operators, and energy enterprises, with data pipelines, IoT asset monitoring, SCADA, ETRM, and energy management platforms.

Turn to Relevant Software when:

• You need pipelines that prepare sensor and operational data for AI forecasting.
• Your forecasts must combine weather data with other signals, such as sky imagery.
• Your project calls for ISO 27001 certification and GDPR compliance.
• You want AI-assisted delivery with up to 50% faster releases and up to 20% fewer bugs.

Projects on record:

• Solar forecasts that pair ML analysis of sky images with weather data: +20% accuracy over a weather-only model.
• Digital twin platform for project execution and operations: −15% planning time.
• Remote device monitoring for a Fortune 500 R&D provider: 10 units in production, 100% secure synchronization, and 9x cognitive assessment capacity.

2. SumatoSoft

SumatoSoft develops predictive maintenance systems and IoT data analytics, with 350+ projects delivered since 2012. The team of 100+ specialists holds ISO 27001 and ISO 9001 certifications.

Turn to SumatoSoft when:

• You want failure predictions built on top of an existing SCADA system.
• Your wind turbines or other field assets stream sensor data that nobody analyzes yet.
• You need a model with measured precision and lead time before rollout.

Projects on record:

• ML failure prediction for a 28-turbine wind farm: 38% less unplanned downtime, fleet availability up from 96.4% to 97.7%, and payback in 9.8 months.

3. Sombra

Sombra builds data and AI solutions for energy and oil and gas companies, including load forecasting and outage prediction. The company has completed 300+ projects since 2013 with 400+ professionals.

Turn to Sombra when:

• Your load forecasts need weather, pricing, and policy variables.
• You want outage prediction based on SCADA and AMI signals and vegetation risk.
• Your failure models should combine sensor data with maintenance history.
• You need SCADA and EMS data streamed into analytics with low latency.

Projects on record:

• AI pre-screening of safety inspection media for a large LPG distributor: manual review time down by up to 80% and inspection throughput up 1.5x.

4. Sii Poland

Sii Poland has served clients since 2006 and employs 7,500+ people. Its utilities practice covers predictive analytics, SCADA and MES systems, digital twins for power plants and wind farms, and NIS2-compliant security audits.

Turn to Sii Poland when:

• You want production forecasts from sensor, device, and weather data for renewable or conventional plants.
• You plan digital twins of power plants or wind farms.
• Your security program must meet NIS2 requirements.

Projects on record:

PLC and SCADA setup, signal and interface testing, Site Acceptance Tests, and grid code testing for new wind farms of a global wind energy company, which shortened commissioning timelines.

5. Simform

Simform builds data platforms and cloud applications with 1,200+ employees and has operated since 2010. The company works with Microsoft, Databricks, and Snowflake technologies.

Turn to Simform when:

• You need real-time telemetry from EV chargers or battery systems.
• Your fleet operations depend on live device data.
• You want Power BI reporting on Azure for utility equipment sales.

Projects on record:

• Telemetry and fleet operations platform for an EV charging and energy management company: 100,000+ vehicles charged and development time cut in half.

6. Integra Sources

Integra Sources develops embedded systems, power electronics, and cloud platforms, with 250+ projects since 2014. Its energy work covers solar monitoring and battery management.

Turn to Integra Sources when:

• You need a cloud backend for high-frequency solar and battery data.
• Your devices report every few seconds and the platform must scale with users.
• You build battery management systems that calculate state of charge and health.

Projects on record:

• AWS cloud for household solar monitoring: data streams every 10 seconds, 25,000 concurrent requests, and 5,000 new users a year.

7. Endava

Endava delivers AI, analytics, and IoT work for energy companies, from predictive maintenance to energy trading and grid modernization. Its certifications include ISO 27001, ISO 9001, and SOC 2 Type 2, and the company trades on the NYSE.

Turn to Endava when:

• You need demand forecasts that trigger action, such as battery charging schedules.
• Your meter data management system needs performance testing at scale.
• You plan energy trading solutions or IT and OT convergence.

Projects on record:

• Peak demand forecasting for Stem, an AI-driven energy storage company: batteries charge off-peak, and electricity costs for Stem’s customers fell by 60%.

What Human Review Should Cover in AI Forecasting

AI can process more signals and update forecasts faster, but it should not remove operational judgment from the process.

Your forecasters, operators, data owners, and analysts each see context that the model may miss, from planned grid events to changes in source data. Build human review into the workflow at the points where those decisions matter most.

1. Forecasters Who Review Exceptions

Your forecasters study the hours where the model disagrees with recent actuals or with their own view. They check the inputs first, since a missing feed often explains a surprise.

2. Operators Who Override With a Reason

Control room staff know about events the model cannot see, such as a planned switching operation or a local festival.

Let them adjust a forecast and log the reason, so your data team can add that signal to future training.

3. Data Owners Who Approve Changes

New sources, changed validation rules, and retrained models all pass through a named owner before they reach production.

That approval step keeps your forecasts stable when systems change.

4. Analysts Who Back-Test Results

Regular back-tests compare forecasts with settled actuals across seasons and sites. Your analysts then see where accuracy slips and which data fix would help most.

This kind of integration between operational systems and data platforms is important across industries. For example, TechPluto has also covered how eMaint merges with modern tech stacks, showing how connected systems can improve information flow and operational efficiency.

Final Thoughts

Reliable AI forecasting starts with the data foundation. Meter, weather, market, asset, and event data need to be aligned, validated, versioned, and monitored before a model can use them consistently. Human review then adds the operational context that data alone cannot always capture.

Use the framework in this article to assess both your current data readiness and the vendors you are considering. Ask each provider how they align time series, handle missing or corrected data, version training sets, monitor live inputs, and support human review after deployment.