of the nutrient concentrations in the stream is usually done on weekly,
biweekly or monthly basis due to limited resources. There is need to estimate
concentration and loads during the period when no data is available. The
objectives of this study were to test the performance of a suite of regression
models in predicting continuous water quality loading data and to determine
systematic biases in the prediction. This study used the LOADEST model which
includes several predefined regression models that specify the model form and
complexity. Water quality data primarily nitrogen and phosphorus from five
monitoring stations in the Neuse River Basin in North Carolina, USA were used
in the development and analyses of rating curves. We found that LOADEST performed generally well in
predicting loads and observation trends with general tendency/bias towards
overestimation. Estimated Total Nitrogen (TN) varied from observation (“true”
load) by -1% to 9%, but for the Total Phosphorus (TP) it ranged from -2% to
27%. Statistical evaluation using R2, Nash-Sutcliff Efficiency (NSE)
and Partial Load Factor (PLF) showed a strong correlation in prediction.
This paper presents
the operation of a Multi-agent system (MAS) for the control of a smart grid.
The proposed Multi-agent system consists of seven types of agents: Single Smart
Grid Controller (SGC), Load Agents (LAGs), a Wind Turbine Agent (WTAG),
Photo-Voltaic Agents (PVAGs), a Micro-Hydro Turbine Agent (MHTAG), Diesel
Agents (DGAGs) and a Battery Agent (BAG). In a smart grid LAGs act as consumers
or buyers, WTAG, PVAGs, MHTAG & DGAGs acts as producers or sellers and BAG
act as producer/consumer or seller/buyer. The paper demonstrates the use of a
Multi-agent system to control the smart grid in a simulated environment. In
order to validate the performance of the proposed system, it has been applied
to a simple model system with different time zone i.e. day time and night time
and when power is available from the grid and when there is power shedding.
Simulation results show that the proposed Multi-agent system can perform the
operation of the smart grid efficiently.