A system comprising: a software system; end devices and energy resources, wherein at least one resource is a rechargeable battery and wherein the software system is arranged to receive from the end devices real-time data on energy supply or usage of the energy resources across an energy system at a location or low voltage network; and wherein the software system is arranged to classify events or behaviours observed in energy usage in the energy system, and comprises: a recurrent neural network (115;119,120) arranged to receive at an input a time series of measurements indicative of energy usage or activity in the energy system derived from the real-time data from the end devices and to identify based on the input 1) a time or occupancy dependent mode of use of the energy system or 2) a high load, long duration event, indicative of use of a particular appliance, disaggregated from the measurements, and to output a scalar real-time value (116,118) representing one or more properties associated with the mode of use or event, being one or more of the device or mode type, start-time of the event or mode, time and power load duration expectation; a prediction engine arranged to calculate a prediction of load or flexibility (!5) in the energy system over a time period and/or a risk profile of the prediction based at least in part on the scalar value, and the software system being further arranged to determine a battery charging plan for charging and/or discharging the rechargeable battery at least in part based on the prediction, the battery being arranged to charge or discharge responsive to the plan. a software system; end devices and energy resources, wherein at least one resource is a rechargeable battery and wherein the software system is arranged to receive from the end devices real-time data on energy supply or usage of the energy resources across an energy system at a location or low voltage network; and wherein the software system is arranged to classify events or behaviours observed in energy usage in the energy system, and comprises: a recurrent neural network (115;119,120) arranged to receive at an input a time series of measurements indicative of energy usage or activity in the energy system derived from the real-time data from the end devices and to identify based on the input 1) a time or occupancy dependent mode of use of the energy system or 2) a high load, long duration event, indicative of use of a particular appliance, disaggregated from the measurements, and to output a scalar real-time value (116,118) representing one or more properties associated with the mode of use or event, being one or more of the device or mode type, start-time of the event or mode, time and power load duration expectation; a prediction engine arranged to calculate a prediction of load or flexibility (!5) in the energy system over a time period and/or a risk profile of the prediction based at least in part on the scalar value, and the software system being further arranged to determine a battery charging plan for charging and/or discharging the rechargeable battery at least in part based on the prediction, the battery being arranged to charge or discharge responsive to the plan. a recurrent neural network (115;119,120) arranged to receive at an input a time series of measurements indicative of energy usage or activity in the energy system derived from the real-time data from the end devices and to identify based on the input 1) a time or occupancy dependent mode of use of the energy system or 2) a high load, long duration event, indicative of use of a particular appliance, disaggregated from the measurements, 1) a time or occupancy dependent mode of use of the energy system or 2) a high load, long duration event, indicative of use of a particular appliance, disaggregated from the measurements, and to output a scalar real-time value (116,118) representing one or more properties associated with the mode of use or event, being one or more of the device or mode type, start-time of the event or mode, time and power load duration expectation; a prediction engine arranged to calculate a prediction of load or flexibility (!5) in the energy system over a time period and/or a risk profile of the prediction based at least in part on the scalar value, and the software system being further arranged to determine a battery charging plan for charging and/or discharging the rechargeable battery at least in part based on the prediction, the battery being arranged to charge or discharge responsive to the plan.