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One step ahead prediction of peak electricity loads based on artificial neural networks (ANN) is presented. Two architectures of ANNs were implemented to produce predictions that were used to generate the final value as an average. The time instants when daily peak loads occur are produced simultaneously. Examples will be given confirming both the feasibility of the method and the need for further...
This paper establishes a robust model to adjust the hourly load level in response to hourly electricity price of a given consumer .The objective of the model is to maximize the utility of the consumer subject to a minimum daily energy supply level, maximum and minimum hourly demand levels, and ramping limits on such demand levels. Unknown price is forecasted through neural network with a confidence...
Demand response (DR) has many beneficiaries in the electricity market. There are independent players who are interested in DR, which include: transmission system owners, distributors, retailers, and aggregators. In this paper DR is introduced as a tradable commodity that can be exchanged between DR buyers and sellers in a pool-based market which is called demand response exchange (DRX). DRX operator...
Load forecasting is important for power systems planning, and, on the operational level, for the grid operators and the balance responsible parties. A decrease in the load forecasting error increases the security of supply, and leads to the decrease in the financial costs for both market participants and the power system as a whole. Different studies have investigated influence of load forecasting...
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