The present disclosure provides a portable standby starting device for a vehicle. The portable standby starting device includes a battery circuit, a load access detecting circuit, and a vehicle starting circuit, wherein the battery circuit is coupled to the load access detecting circuit and the vehicle starting circuit, and is configured to supply power to the load access detecting circuit and the vehicle starting circuit; the load access detecting circuit is coupled to the vehicle starting circuit, and is configured to detect whether the vehicle starting circuit is connected to a vehicle load; the vehicle starting circuit is configured to, when the load access detecting circuit detects the connection of the vehicle load, output a vehicle starting current for controlling an ignition operation performed for the vehicle.

Various embodiments include a method for certifying at least one installation-specific amount of energy, wherein the installation-specific amount of energy has been generated or consumed by an energy installation of an energy system. The method may include: capturing a temporal measured signal accumulated in relation to the energy system of a measured variable associated with the amount of energy; transmitting the captured signal to a test unit external to the energy system; disaggregating the transmitted signal using the test unit, and ascertaining the installation-specific amount of energy by way of the disaggregation; issuing a certificate regarding the ascertained installation-specific amount of energy; and receiving the issued certificate at the energy system.

Embodiments implement non-intrusive load monitoring using machine learning. A trained convolutional neural network (CNN) can be stored, where the CNN includes a plurality of layers, and the CNN is trained to predict disaggregated target device energy usage data from within source location energy usage data based on training data including labeled energy usage data from a plurality of source locations. Input data can be received including energy usage data at a source location over a period of time. Disaggregated target device energy usage can be predicted, using the trained CNN, based on the input data.

A site management system has a site management device located on a fielded site, which has a controller unit integral with a power provision unit, and the power provision unit receives an input voltage via a conductor cable and delivers power to one or more receptacles. Additionally, the system has a plurality of remote devices communicatively coupled to the site management device over a wireless network and at least one off-site computing device communicatively coupled to the site management device. Further, the system has a processor on the controller unit that communicatively couples with at least one remote device, receives data indicative of a unique identifier from the wireless remote device, and determines whether the unique identifier correlates with a remote device of an individual who is permissively on the fielded site. In addition, the processor transits data indicative of the individual and data indicative of whether the individual is permissively on the fielded site to the off-site computing device or a site manager's remote device.

An electric energy supply management method includes having a certifier system define a reference electric power profile for an electric apparatus, and having the certifier system provide a device coupled to the electric apparats and to a socket that delivers electric energy provided by an electric energy supplier. The device is associated only to the electric apparatus through the reference electric power profile. The method also includes having a user of the electric apparatus couple the electric apparatus to the socket through the device, and having the device check that the electric apparatus is coupled to the socket by comparing a measured electric power profile of the electric apparatus to the reference electric power profile. If the check has a positive outcome, the method has the device collect measurements about the electric power used by the electric apparatus and certify them as energy consumptions of the electric apparatus.

A stackable power storage system is herein described. It comprises a plurality of power modules connectable to each other into a power stack, each one of the power modules comprising at least of a bottom and a top identical to another one of the power modules. The bottom of a top one of the power modules is at least partially nestable in a bottom one of the power modules, The modules comprise connectors connected connectable between power modules when the power modules are stacked thereby at least partially nested relative to each other. Power is transferred between the power modules through the connectors.

Embodiments implement non-intrusive load monitoring using a novel learning scheme. A trained machine learning model configured to disaggregate device energy usage from household energy usage can be stored, where the machine learning model is trained to predict energy usage for a target device from household energy usage. Household energy usage over a period of time can be received, where the household energy usage includes energy consumed by the target device and energy consumed by a plurality of other devices. Using the trained machine learning model, energy usage for the target device over the period of time can be predicted based on the received household energy usage.

Provided is a power supply device in which the reliability of a voltage output is improved, and also provided is a system comprising the power supply device and an electric tool. The power supply device (1) is provided with: a secondary battery cell (2a); an output unit connected to an electric tool and supplying the power of the secondary battery cell (2a) to the electric tool; and switching elements (Q1), (Q2) capable of interrupting a current path from the secondary battery cell (2a) to the electric tool. The switching element (Q1) switches whether or not to interrupt the current path not by being controlled by a control circuit (12) but in accordance with whether the electric tool connected to the output unit has a shorting bar (59) or not.

Information indicating a source of electric current can be transmitted alongside or within the electric current itself, enabling downstream recipients to identify a source of their electricity. The information may be embedded directly into the electric current, such as by adding a modulated carrier signal to an alternating current before transmitting the current to a downstream recipient.

A load power monitoring system includes: an external power supply source; a renewable energy source configured to generate power or store a power applied from the external power supply source, and discharge the generated or stored power; a distribution board configured to distribute a power applied from the external power supply source or the renewable energy source to an electronic device; a power measurement device configured to detect power amount data of at least one of the external power supply source and the renewable energy source; a second power measurement device configured to detect power amount data distributed to the electronic device; and a monitoring server configured to collect power amount data detected by each of the power measurement devices and monitor a power of a load on the basis of the collected power amount data.