Abstract
Nowadays electricity load forecasting is important to further minimize the cost of day-ahead energy market. Load forecasting can help utility operators for the efficient management of a demand response program. Forecasting of electricity load demand with higher accuracy and efficiency can help utility operators to design reasonable operational planning of generation units. But solving the problem of load forecasting is a challenging task since electricity load is affected by previous history load, several exogenous external factors (i.e., weather variables, social variables, working day or holiday), time of day, and season of the year. To solve the problem of short-term load forecasting (STLF) and further improve the forecasting accuracy, in this paper we have proposed a novel hybrid STLF model with a new signal decomposition and correlation analysis technique. To this end, load demand time series is decomposed into some regular low frequency components using improved empirical mode decomposition (IEMD). To compensate for the information loss during signal decomposition, we have incorporated the effect of exogenous variables by performing correlation analysis using T-Copula. From the T-Copula analysis, peak load indicative binary variable is derived from value at risk (VaR) to improve the load forecasting accuracy during peak time. The data obtained from IEMD and T-Copula is applied to deep belief network for predicting the future load demand of specific time. The proposed data driven method is validated on real time data from the Australia and the United States of America. The performance of proposed load forecasting model is evaluated in terms of mean absolute percentage error (MAPE) & root mean square error (RMSE). Simulation results verify that, the proposed model provides a significant decrease in MAPE and RMSE values compared to traditional empirical mode decomposition based electricity load forecasting.
Highlights
Electricity load forecasting is essential for the utility provider to manage the demand response program efficiently in day ahead energy market
Investment and transaction are based on accurate electricity load forecasting
Even though we have investigated improved empirical mode decomposition (IEMD) to suppress the limitation of traditional empirical mode decomposition (EMD), good results can be obtained by just mirroring the extrema close to edges [22]
Summary
Electricity load forecasting is essential for the utility provider to manage the demand response program efficiently in day ahead energy market. From the information of electricity load demand of consumers, utility providers can estimate how much electric energy is needed in the grid. The objective of the utility provider is to minimize the cost of energy production and purchasing [1]. According to Bunn and Farmer [3], [4], the cost of electric utility operator is saved by 10 million pounds due to 1% decrease of load forecasting errors. Investment and transaction are based on accurate electricity load forecasting. Accurate electricity load forecasting is prerequisite for making secure, reliable and economic operation of power system [5]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have