A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers

For multiple heterogeneous multicore server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large-scale server systems in current and future data centers. The multicore processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multicore server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.

Low frequency ultrasound debridement (Sonoca-185) in acute wound management: a case study

There are numerous evidence-based wound debridement techniques that promote wound healing. However, some of these techniques may cause discomfort and pain for the patient and can be costly for the health care provider. A new, non-invasive wound debridement technique known as low-frequency ultrasonic debridement (LFUD) has been used for the removal of unhealthy tissue and bacterial load in wound management in the clinical setting. This paper reports the use of LFUD by a skin integrity clinical nurse consultant (CNC) as an adjuvant wound debridement and healing technique in a patient with a parastomal abscess. LFUD was found to benefit this patient in terms of expedited wound healing and increased comfort, enabling the patient to have a successful skin graft that led to complete wound closure and discharge from hospital in a timely manner.

Restoration rocks: integrating abiotic and biotic habitat restoration to conserve threatened species and reduce fire fuel load

With rapid urban expansion, biodiversity conservation and human asset protection often require different regimes for managing wildfire risk. We conducted a controlled, replicated experiment to optimise habitat restoration for the threatened Australian pink-tailed worm-lizard, Aprasia parapulchella while reducing fire fuel load in a rapidly developing urban area. We used dense addition of natural rock (30 % cover) and native grass revegetation (Themedatriandra and Poasieberiana) to restore critical habitat elements. Combinations of fire and herbicide (Glyphosate) were used to reduce fuel load and invasive exotic species. Rock restoration combined with herbicide application met the widest range of restoration goals: it reduced fire fuel load, increased ant occurrence (the primary prey of A. parapulchella) in the short-term and increased the growth and survival of native grasses. Lizards colonised the restored habitat within a year of treatment. Our study documents an innovative way by which conflicts between biodiversity conservation and human asset protection can be overcome.

Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey.

Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

Power management and control strategies for efficient operation of a solar power dominated hybrid DC microgrid for remote power applications

In this paper, a hybrid DC microgrid consisting of a diesel generator with a rectifier, a solar photovoltaic (PV) system, and a battery energy storage system is presented in relation to an effective power management strategy and different control techniques are adopted to power electronic interfaces. The solar PV and battery energy storage systems are considered as the main sources of energy sources that supply the load demand on a daily basis whereas the diesel generator is used as a backup for the emergency operation of the microgrid. All system components are connected to a common DC bus through an appropriate power electronics devices (e.g., rectifier systems, DC/DC converter). Also a detailed sizing philosophy of all components along with the energy management strategy is proposed. Energy distribution pattern of each individual component has been conducted based on the monthly basis along with a power management algorithm. The power delivered by the solar PV system and diesel generator is controlled via DC-DC converterand excitation controllers which are designed based on a linearquadratic regulator (LQR) technique as as proportional integral (PI)controllers. The component level power distribution is investigatedusing these controllers under fluctuating load and solar irradiationconditions and comparative results are presented.