TY  - CONF
A1  - Hassan, S.
A1  - Khosravi, A.
A1  - Jaafar, J.
A1  - Raza, M.Q.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908614947&doi=10.1109%2fSYSOSE.2014.6892467&partnerID=40&md5=0a6e161e4834d9fa9ec25c37f78b36ac
EP  - 84
Y1  - 2014///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781479952274
N2  - With the emergence of smart power grid and distributed generation technologies in recent years, there is need to introduce new advanced models for forecasting. Electricity load and price forecasts are two primary factors needed in a deregulated power industry. The performances of the demand response programs are likely to be deteriorated in the absence of accurate load and price forecasting. Electricity generation companies, system operators, and consumers are highly reliant on the accuracy of the forecasting models. However, historical prices from the financial market, weekly price/load information, historical loads and day type are some of the explanatory factors that affect the accuracy of the forecasting. In this paper, a neural network (NN) model that considers different influential factors as feedback to the model is presented. This model is implemented with historical data from the ISO New England. It is observed during experiments that price forecasting is more complicated and hence less accurate than the load forecasting. © 2014 IEEE.
N1  - cited By 13; Conference of 9th International Conference on System of Systems Engineering, SoSE 2014 ; Conference Date: 9 June 2014 Through 13 June 2014; Conference Code:114536
ID  - scholars4832
TI  - Electricity load and price forecasting with influential factors in a deregulated power industry
SP  - 79
KW  - Costs; Distributed power generation; Forecasting; Neural networks; Smart power grids; Systems engineering
KW  -  Demand response programs; Distributed generation technologies; Electricity generation; Forecasting models; Influential factors; Neural network model; Power industry; Price forecasting
KW  -  Electric load forecasting
AV  - none
ER  -