# Forewatt

> Forewatt forecasts hourly electricity prices and plans your production at the lowest cost. For energy-intensive industry: 8–17% savings, no works.

Forewatt forecasts the electricity price hour by hour and automatically generates the optimal operating plan for your high-demand processes. No works. No changes to your machinery. No need to switch supplier.

## The problem

Electricity is your largest variable cost. And it is still managed reactively.

- **The price changes 24 times a day**: The OMIE market publishes the next day’s hourly prices at 2 pm. Without a system that processes them and acts accordingly, that knowledge arrives too late, or not at all.
- **Demand peaks are penalised for months**: Starting several machines at once during peak hours does not just cost more at that moment: it raises the capacity charge on the whole bill for the following months.
- **Your processes have complex physical constraints**: In foundry, chemicals or ceramics, high-temperature processes have thermal inertia, minimum cycle times and shift constraints that no smart meter can model.
- **Existing solutions are not built for industrial SMEs**: Energy management systems on the market are expensive, rigid and require infrastructure that a mid-sized company does not have, and should not need. Forewatt is different.

## How it works

Forewatt connects the electricity market with your industrial operations. The result: a daily or weekly work plan that minimises energy cost while respecting every physical and production constraint of your process.

1. **Real-time electricity market data**: Direct connection to the ESIOS API (Red Eléctrica, Spain’s grid operator) for hourly OMIE market prices and renewable generation data. No scraping, no intermediaries.
2. **Price forecasting with Machine Learning**: An XGBoost model trained on the last 14 months of data forecasts the price curve for the coming hours. It factors in the renewable generation mix, temperature and historical volatility patterns.
3. **Load scheduling optimisation**: A combinatorial optimisation algorithm decides which equipment to run, when and at what load. It accounts for physical constraints (thermal inertia, minimum temperatures), work shifts and open production orders.
4. **An actionable plan for your operations team**: The system does not automate or modify your machinery. It produces a daily or weekly plan that your operators carry out from the web interface. The simulation shows the expected cost with and without optimisation: verifiable and auditable.

## Industries

- **Foundry and metallurgy**: Induction furnaces, scheduled melts and complex thermal inertia. Forewatt optimises when to switch on each furnace, at what power and which combination of parts to load, to maximise efficiency and avoid demand penalties.
- **Chemicals and pharmaceuticals**: Reactors, autoclaves and batch processes with defined operating windows. Forewatt sequences operations to concentrate consumption in the cheapest time slots while respecting process times.
- **Food and meat processing**: Cold stores, freezing tunnels and industrial cooking. Large consumers with real operational flexibility to shift load without compromising the cold chain or food safety standards.
- **Ceramics, glass and materials**: Tunnel kilns and continuous firing with long temperature cycles. Forewatt models the thermal inertia of the process and finds the best start time to make the most of night-time and weekend off-peak prices.

## Why Forewatt

- **It does not tell you what happened. It tells you what to do.** A smart meter shows past consumption. Forewatt delivers a concrete action plan for tomorrow, next week and next month, with the expected cost of each decision before you make it.
- **Zero changes to your plant.** Forewatt does not automate your furnaces, install extra hardware or require changes to your current infrastructure. All you need is a computer with an internet connection. The system builds the plan; your operators carry it out.
- **Savings are audited, not estimated.** Every simulation records the expected cost with and without optimisation. The system generates automatic reports with the actual accumulated ROI, ready for finance, audits or reporting to public bodies.

## Case study: Casuso Propellers S.A.

Manufacturer of marine propellers and hydraulic turbines in Entrambasaguas, Cantabria (Spain). Two electric steel-melting furnaces —16 and 6 tonnes— with an electricity spend of €35,000–40,000 a month. Melts must reach 1,450 ºC and hold that temperature for at least two hours: a physical constraint no generic system can handle without specific modelling.

- Monthly electricity spend: €35,000–40,000
- Estimated potential savings: 8–17% a year
- Time to first simulation: 48 hours
- Infrastructure required: A computer and an internet connection. No works, no hardware.

## Contact

- Form: https://forewatt.com/en/#contact
- Email: info@evenbytes.com
- Company: Evenbytes S.L. (https://evenbytes.com), Santander, Spain

## Pages

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