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Fig. 1. Example Facility Load In our continuous effort regarding energy management sav- ing, we opted to focus on energy peaks occurring on our site, as shown in Figure 1. For industrial and commercial customers, electricity prices are based on two elements: actual usage (kWh), and a penalty based on the highest point of demand (or peak) within the billing period (weekly, monthly, or annually). Demand is often calculated using demand intervals, which are typically a short timeframe (e.g., 15 minutes) during which overall usage is aggregated and tracked as a total. The average calculated is the kW demand for this period. Charges based on demand can be controlled or reduced by the customer by modifying energy usage. For example, peak shaving is the ability to control usage from an energy supplier during intervals of high demand for purposes of limiting or reducing demand penalties for the billing period. Our site, also hosts a Data Center solution along with a 300 kWh battery which is used as a backup solution for our data centers to prevent any power outage.

Figure 1 Example Facility Load In our continuous effort regarding energy management sav- ing, we opted to focus on energy peaks occurring on our site, as shown in Figure 1. For industrial and commercial customers, electricity prices are based on two elements: actual usage (kWh), and a penalty based on the highest point of demand (or peak) within the billing period (weekly, monthly, or annually). Demand is often calculated using demand intervals, which are typically a short timeframe (e.g., 15 minutes) during which overall usage is aggregated and tracked as a total. The average calculated is the kW demand for this period. Charges based on demand can be controlled or reduced by the customer by modifying energy usage. For example, peak shaving is the ability to control usage from an energy supplier during intervals of high demand for purposes of limiting or reducing demand penalties for the billing period. Our site, also hosts a Data Center solution along with a 300 kWh battery which is used as a backup solution for our data centers to prevent any power outage.

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Fig. 2. Overall Architecture key, value pair of the collected readings along with a timestamp indicating the time of collect. ~ The IoT agent retrieves data from our weather service such as outside temperature and humidity. We also collected the energy bills from the utility specific to our building and integrated them to our IoT agent to be sent to our cloud repository. In addition, our IoT agent as detailed in [5] collects the topological information and timeseries from the three on premise systems. The topological information is merged into one ontology representation as shown in Figure 3. The semantic representation of the three systems enables the inter- connectivity of elements into one single view. For example, in Figure 3, BMS1 is a Buildings Management System, it is connected to an Automation Server AS—11 which controls a Compressor Comp1. This energy consumption of the com- pressor is measured by the Power Monitor system PM1. In addition to the interconnectivity, the semantic representation provides a formal representation of the systems with reasoning capabilities. For example, the relation IsLocatedIn shown in Figure 3 declared as transitive will be relied upon by a inference engine part of the ontology store to answer queries such as Find all temperature sensors in Building A”. The inference engine will rely on such properties and their nature to answer such query, for example, in Figure 3, sensor1 is located in Room 202A which is located in the West2 area, on the floor L2 of the building, since IsLocatedIn is declared as transitive, the inference engine will deduce that the sensor! is actually located in the building. Transitivity is given as an example, however, other relations are deduced based on symmetricity and inversability.
Fig. 2. Overall Architecture key, value pair of the collected readings along with a timestamp indicating the time of collect. ~ The IoT agent retrieves data from our weather service such as outside temperature and humidity. We also collected the energy bills from the utility specific to our building and integrated them to our IoT agent to be sent to our cloud repository. In addition, our IoT agent as detailed in [5] collects the topological information and timeseries from the three on premise systems. The topological information is merged into one ontology representation as shown in Figure 3. The semantic representation of the three systems enables the inter- connectivity of elements into one single view. For example, in Figure 3, BMS1 is a Buildings Management System, it is connected to an Automation Server AS—11 which controls a Compressor Comp1. This energy consumption of the com- pressor is measured by the Power Monitor system PM1. In addition to the interconnectivity, the semantic representation provides a formal representation of the systems with reasoning capabilities. For example, the relation IsLocatedIn shown in Figure 3 declared as transitive will be relied upon by a inference engine part of the ontology store to answer queries such as Find all temperature sensors in Building A”. The inference engine will rely on such properties and their nature to answer such query, for example, in Figure 3, sensor1 is located in Room 202A which is located in the West2 area, on the floor L2 of the building, since IsLocatedIn is declared as transitive, the inference engine will deduce that the sensor! is actually located in the building. Transitivity is given as an example, however, other relations are deduced based on symmetricity and inversability.
Fig. 3. Contextual and Timeseries Data Extracted from the Systems
Fig. 3. Contextual and Timeseries Data Extracted from the Systems
Fig. 4. Energy Storage Application Model
Fig. 4. Energy Storage Application Model
Listing 1: SPARQL Query: Power Consumption of all the Chillers Fig. 5. Model Predictive Control Architecture
Listing 1: SPARQL Query: Power Consumption of all the Chillers Fig. 5. Model Predictive Control Architecture
Fig. 6. Four Day Energy Storage Deployment.
Fig. 6. Four Day Energy Storage Deployment.
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Computer ScienceData Modeling
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