The load forecast solution for greenhouses is an offline model structure built on data from energy consumption and weather observations.
The load forecast solution for greenhouses, is build on the collection of data from greenhouse consumers and weather observations to derive an offline model structure for load forecasting in greenhouses. Thus, the solution established an online setup for load forecasting where model parameters are adapted to the actual dynamics of consumption and weather.
A model based predictive control of supply temperatures leads to reduced heating loss and pumping costs. This means saving of both resources and money for greenhouse owners applying our solution.
Greenhouses are due to their construction, highly sensitive to external factors of weather and season such as solar radiation and out-door humidity. All together this implies that the heat load is fluctuating much more for greenhouses than for domestic or factory buildings. Load forecasting including automatic calibration of model parameters is the answer to overcome this challenge. Some dimensions of the project solution includes:
Greenhouses consumption are recorded hourly as accumulated energy, accumulated volume flow, temperature forward and return. Weather observations such as wind speed, wind direction, and solar radiation radiation is recorded hourly (running average logged every hour, raw data sampled every 1 minute). Forecast are obtained at the same place as the observation, with the same parameters, so here it will be necessary to make the same time adjustment as for the observations.
Time correction for geographic displacement
To compensate for a geographic displacement between observation and a specific greenhouse, it is necessary to calculate a time correction.
Stochastic model for greenhouse demand
A universal model is developed for greenhouses, but for better adaption to single greenhouse parameters must be adapted. Model parameters should be generated from observation obtained over a time horizon of one week (this we don’t know yet). It is important that the model is transparent, and defined by parameters, so it doesn’t need to be a black box model, and secondly generate a load forecast time series form weather forecast time series (hourly values, 124 hours ahead).
Different scenarios are simulated to find the cost minimum of time depending prices, pushing or pulling the load in time.
Forecast services for optimal control of supply temperatures to greenhouses
Forecast services are crucial for optimal decision-making, production planning, trading of power and for control of district heating network. In this section we briefly describe the list of forecast services needed, discuss the statistical forecast characteristics, and illustrate how the forecasts can be used in optimal control and decision making.
It is clear that forecasts are needed both on a day-to-day basis, e.g., in order to provide input for the market clearing, and for the optimal production planning, as well as on a shorter horizon, e.g. in order to use the flexibility of the distributed energy resources to control the electricity load.
The predictive controllers considered are based on forecasts of load, prices, etc. The forecast services needed depend on whether Direct Control (DC) or Indirect Control (IC, also referred to as or control-by-price) is implemented:
- Load (demand or flexibility) forecasts (for both DC and IC)
- Price forecasts (only for IC)
- State forecasts (e.g. room temperature) (only for DC)
In all cases it is assumed the appropriate meteorological forecasts are available.
The project of CITIES Research Center is expected to continue on improving the solution until March 2017.
The solution is connected to the project on smart temperature control in greenhouses.