Abstract
The problem of Photovoltaic (PV) power generation forecasting is becoming crucial as the penetration level of Distributed Energy Resources (DERs) increases in microgrids and Virtual Power Plants (VPPs). In order to improve the stability of power systems, a fair amount of research has been proposed for increasing prediction performance in practical environments through statistical, machine learning, deep learning, and hybrid approaches. Despite these efforts, the problem of forecasting PV power generation remains to be challenging in power system operations since existing methods show limited accuracy and thus are not sufficiently practical enough to be widely deployed. Many existing methods using long historical data suffer from the long-term dependency problem and are not able to produce high prediction accuracy due to their failure to fully utilize all features of long sequence inputs. To address this problem, we propose a deep learning-based PV power generation forecasting model called Convolutional Self-Attention based Long Short-Term Memory (LSTM). By using the convolutional self-attention mechanism, we can significantly improve prediction accuracy by capturing the local context of the data and generating keys and queries that fit the local context. To validate the applicability of the proposed model, we conduct extensive experiments on both PV power generation forecasting using a real world dataset and power consumption forecasting. The experimental results of power generation forecasting using the real world datasets show that the MAPEs of the proposed model are much lower, in fact by 7.7%, 6%, 3.9% compared to the Deep Neural Network (DNN), LSTM and LSTM with the canonical self-attention, respectively. As for power consumption forecasting, the proposed model exhibits 32%, 17% and 44% lower Mean Absolute Percentage Error (MAPE) than the DNN, LSTM and LSTM with the canonical self-attention, respectively.
Highlights
Microgrids and Virtual Power Plants (VPPs) are two remarkable solutions for a reliable supply of electricity in a power system [1]
The experimental results of power generation forecasting using the real world datasets show that the Mean Absolute Percentage Error (MAPE) of the proposed model are much lower, by 7.7%, 6%, 3.9% compared to the
REVIEWforecasting results for each method, i.e., Deep Neural Network (DNN), Long Short-Term Memory (LSTM), LSTM 10 of 17the shows the2020, PV13, power generation with canonical self-attention and our proposed Convolutional Self-Attention LSTM model, respectively
Summary
Microgrids and Virtual Power Plants (VPPs) are two remarkable solutions for a reliable supply of electricity in a power system [1]. Microgrids are power systems comprising Distributed Energy. Resources (DERs) and electricity end-users, possibly with controllable elastic loads, all deployed across a limited geographic area [2]. A VPP is a flexible representation of a portfolio of DERs that can be used to make contracts in the wholesale market and to offer services to the system operator [4]. Energies 2020, 13, 4017 management for microgrids and a VPP [5,6].
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.
