Electrification of rural areas has always been a challenge. In addition, large livestock farms such as dairy farms, also face environmental problems due to inappropriate treatment of animal wastes. Therefore, improvement of the electrical system in farms vis-a-vis efficient waste management is of great importance to industry. In this paper, a combined cooling, heating, and power (CCHP) microgrid model was built to improve system efficiency of energy utilization and lessen environmental problems caused by animal wastes based on multi-objective optimization. In this proposed model, the animal manure is used to produce biogas, which is used to feed internal combustion engines (ICEs), gas boilers, and absorption chillers. The generated electricity would offset that would otherwise be bought from main grids. PV generation is introduced to further increase the renewable fraction of energy contents. The electrical storage is also included to balance power demand and power supply. To increase system efficiency of energy utilization, coolant water from ICEs, plus their exhaust, could be utilized based on the thermal storage modeled, by inclusion of absorption refrigerator and heat exchanger. The final goal is to maximize energy output from distributed energy resources (DERs) and meanwhile minimize daily system operating cost. Extra electricity generated beyond the load demand could be used to charge battery storage. In this way, system energy cost is reduced with energy system improvement and reconfiguration, and surrounding environmental problems due to animal wastes are also lessened.

This paper proposes optimal strategies for control of distributed Energy Storage Systems (ESSs) to minimize Demand Charge (DC) cost and maximize local Photovoltaic (PV) utilization for Commercial & Industrial Buildings. Reducing DC cost has a direct impact on customer's electricity bill while increasing local PV utilization can help the efficient operation of distribution systems. Model-based optimization methods are presented for each individual objective. Furthermore, a multi-objective control strategy has been implemented and studied to show the possibility of stacking both services on a single ESS. Simulation studies are carried out to show optimal operation of a sample ESS unit in each scenario.

This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis.

In recent years, developments of the behind the meter energy managements systems (BTM-EMSs) has been considered as an effective approach to manage the energy usage of the industrial/commercial units. As the one of the most important missions, BTM-EMSs are responsible to reduce the customers' monthly demand peaks which is rewarded by significant decrease in the monthly demand charge. However, the unpredicted behavior of individual electricity loads casts a shadow over the profitability of installing BTM storage units. In this paper, a data driven demand charge management solution (DCMS) is proposed to find and realize the minimum achievable monthly demand peak, also called demand charge threshold (DCT), by appropriate battery storage charging and discharging. The proposed approach uses the last few months load profile to calculate time series of DCTs and then searches for the similar DCT time series of other observed loads stored in the data set. Finally, the obtained similar time series are employed to forecast the DCT of the coming month. The efficiency of the proposed approach is validated through the simulation studies on the real value load data.

Most data streams usually are multi-dimensional, high-speed, and contain massive volumes of continuous information. They are seen in daily applications, such as telephone calls, retail sales, data center performance, and oil production operations. Many analysts want insight into the behavior of this data. They want to catch the exceptions in flight to reveal the causes of the anomalies and to take immediate action. To guide the user in finding the anomalies in the large data stream quickly, we derive a new automated neighborhood threshold marking technique, called AnomalyMarker. This technique is built on cell-based data streams and user-defined thresholds. We extend the scope of the data points around the threshold to include the surrounding areas. The idea is to define a focus area (marked area) which enables users to (1) visually group the interesting data points related to the anomalies (i.e., problems that occur persistently or occasionally) for observing their behavior; (2) discover the factors related to the anomaly by visualizing the correlations between the problem attribute with the attributes of the nearby data items from the entire multi-dimensional data stream. Mining results are quickly presented in graphical representations (i.e., tooltip) for the user to zoom into the problem regions. Different algorithms are introduced which try to optimize the size and extent of the anomaly markers. We have successfully applied this technique to detect data stream anomalies in large real-world enterprise server performance and data center energy management.