Abstract
In the current trend of consumption, electricity consumption will become a very high cost for the end-users. Consumers acquire energy from suppliers who use short, medium, and long-term forecasts to place bids in the power market. This study offers a detailed analysis of relevant literature and proposes a deep learning methodology for forecasting industrial electric usage for the next 24 h. The hourly load curves forecasted are from a large furniture factory. The hourly data for one year is split into training (80%) and testing (20%). The algorithms use the previous two weeks of hourly consumption and exogenous variables as input in the deep neural networks. The best results prove that deep recurrent neural networks can retain long-term dependencies in high volatility time series. Gated recurrent units (GRU) obtained the lowest mean absolute percentage error of 4.82% for the testing period. The GRU improves the forecast by 6.23% compared to the second-best algorithm implemented, a combination of GRU and Long short-term memory (LSTM). From a practical perspective, deep learning methods can automate the forecasting processes and optimize the operation of power systems.
Highlights
Types of electric load forecasting techniques fall into three main categories regarding the forecast horizon: STLF, MTLF, and LTLF (Long term load forecasting)
We found that the deep Gated recurrent units (GRU) network performs better than the combined GRU + Long short-term memory (LSTM) network
The novelty of the work is the proposed framework applied for industrial load curves, the analysis of the best architecture, and the scalability of the deep neural networks using a simple complexity index
Summary
Types of electric load forecasting techniques fall into three main categories regarding the forecast horizon: STLF (short-term load forecasting), MTLF (medium-term load forecasting), and LTLF (Long term load forecasting). The approach presented in the article for 24 h ahead forecast facilitates the acces to the DAM (day ahead market) and Intra-day market to minimize the difference between real and forecasted values. This difference is mandatory to be balanced on the balancing market, which represents a financial problem for the supplier. Most large non-residential consumers have a single tarriff (price/MWh) and few electricity suppliers offer time differenciated prices and time of use tarriffs. This single tariff is calculated to cover the expences with portfolio balancing. Every supplier needs to balances the portfolio of clients by being a balancing responsible party (BRP) or by submiting these responsibility to other parties
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
