Energy Optimization: Factory Power AI on GPU

The Challenge: £2.8 Million in Annual Energy and Rising Fast

A glass container manufacturer in Yorkshire operates two melting furnaces, eight forming machines, four annealing lehrs, and an extensive compressed air network consuming 28 GWh of energy annually at a cost of £2.8 million — and rising with every energy price adjustment. The plant’s energy management relies on manual scheduling rules developed decades ago: furnaces ramp at fixed times regardless of grid pricing, compressed air runs at constant pressure regardless of demand, and the annealing lehrs follow conservative temperature profiles designed for worst-case glass thickness. The energy manager knows that significant savings exist in dynamic load shifting, demand-response participation, and optimised process parameters, but the interactions between systems are too complex for spreadsheet-based optimisation.

The plant has 340 power meters, temperature sensors, and flow meters feeding data into a SCADA system. The raw data is available but has never been used for predictive optimisation. Third-party energy management platforms require exporting operational data to cloud services — the plant’s parent company prohibits sharing manufacturing process data with external platforms.

AI Solution: Reinforcement Learning for Energy Optimisation

A GPU-accelerated reinforcement learning agent learns to control energy-intensive systems dynamically. The RL agent observes current production schedules, grid energy prices (half-hourly), ambient temperature, equipment states, and compressed air demand, then outputs optimal setpoints: furnace ramp timing aligned with cheaper energy periods, variable-speed drive settings for compressed air compressors matching actual demand, and annealing lehr profiles tailored to the specific glass thickness being produced.

The model trains on historical SCADA data — two years of sensor readings correlated with energy bills and production records — then runs in real time on a dedicated GPU server, updating setpoint recommendations every 15 minutes. All data processing occurs on UK-based infrastructure.

GPU Requirements

RL training for multi-system energy optimisation requires processing millions of state-action-reward tuples from historical data. The state space includes 340 sensor readings, production parameters, and external variables. Training converges faster on GPU, and real-time inference with the trained policy requires GPU acceleration for the neural network evaluations.

For the 15-minute inference cycle, any GPU provides more than adequate performance. The RTX 5090 balances training speed and cost effectively. Private AI hosting ensures operational data stays within GDPR-compliant infrastructure.

Recommended Stack

• Stable-Baselines3 with SAC (Soft Actor-Critic) or PPO for the RL energy agent.
• PyTorch as the neural network backend with CUDA acceleration.
• Apache Kafka or OPC UA for streaming real-time sensor data from SCADA.
• InfluxDB for time-series storage of energy data, predictions, and realised savings.
• Grafana for the energy management dashboard showing real-time optimisation impact.

For generating energy reports and sustainability documentation, add an LLM via vLLM. Integrate document AI to process energy invoices and automatically validate savings against utility bills.

Cost Analysis

A 12% reduction on £2.8 million annual energy costs saves £336,000 per year. The three primary savings levers are: load shifting furnace operations to off-peak tariff periods (5% saving), demand-matched compressed air control (4% saving), and optimised annealing profiles (3% saving). The dedicated GPU server cost is recovered within the first two weeks of operation.

Additional revenue comes from demand-response participation. By enabling the RL agent to reduce load during grid stress events, the plant can earn £15,000–£25,000 annually in demand-response payments from National Grid ESO. The plant also benefits from reduced carbon emissions — approximately 1,200 tonnes CO2e annually — supporting ESOS compliance and corporate sustainability targets.

Getting Started

Export two years of SCADA data including all power meters, temperature sensors, production schedules, and half-hourly energy billing data. Train the RL agent on the compressed air system first — it offers the quickest wins with the lowest risk — before extending to furnace scheduling and annealing optimisation. Deploy in advisory mode for 30 days, with the energy manager reviewing recommendations before setpoint changes are applied automatically.

GigaGPU provides UK-based dedicated GPU servers for industrial AI optimisation. Add an AI chatbot for energy team queries against operational data and historical consumption patterns.