Planning for electricity demand is a key factor for the success in the development of any countries. Such success can only be achieved if the demand for electricity is predicted correctly and accurately. This study introduces a new hybrid approach that combines Principle Component Regression (PCR) and Back-Propagation Neural Networks (BPNN) techniques in order to improve the accuracy of the ele...

Power System planning starts with Electric load (demand) forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future project...

It was the overall objective of this study to close the methodological gap between the specification of explicit constrained optimisation models and dual systems of output supply/input demand equations for representing multi-output, multi-input agricultural supply behaviour. The pursuit of this goal was motivated by the hope to combine the flexibility of programming models in representing techn...

energy is considered as an important input for agricultural products. a decrease in energy subsidy and its demand reduces agricultural outputs and raises the production costs. changes in production and costs may significantly affect welfare of the agricultural producers. the main objective of this study was to analyze the impact of increased energy prices (reduction in the subsidy) on the agric...

productivity shocks are one of the most important factors that influence the firms ‘ decisions on he optimal consumption of production inputs. unobservable productivity shocks create selection and simultaneity problems causing biased estimates of production function by ordinary least squares. however, to estimate coefficients associated with production function by levinsohn and petrin technique...

We consider the problem of allocating resources to satisfy demand requirements varying over time. The input specifies a demand for each timeslot. Each resource is specified by a start-time, end-time, an associated cost and a capacity. A feasible solution is a multiset of resources such that at any point of time, the sum of the capacities offered by the resources is at least the demand requireme...

Planning for electricity demand is a key factor for the success in the development of any countries. Such success can only be achieved if the demand for electricity is predicted correctly and accurately. This study introduces a new hybrid approach that combines Principle Component Regression (PCR) and Back-Propagation Neural Networks (BPNN) techniques in order to improve the accuracy of the ele...

Residential thermostatically controlled loads (TCLs), such as refrigerators and air conditioners, can be used for fast time-scale demand response to manage energy imbalances in power systems. Many direct load control strategies rely on information exchange between a central controller and loads in realtime. This project investigates the effects of limited bandwidth, latencies, and lost measurem...

The objective of this paper is to introduce models to determine an effective, feasible, and cost-efficient strategy for Saudi Arabia’s water supply system to meet its projected demand in 2025. This paper uses cost minimizing and production maximizing approaches to build the models. The water management system is divided into three processes–desalination, distribution, and wastewater treatment. ...