article · Cogent Engineering
Integrating solar photovoltaic power into modern smart grids requires dependable forecasting to manage intermittent energy supplies and shifting consumer demand. To address this, a novel hybrid deep learning architecture combines a feed-forward neural network, long short-term memory networks, and multi-objective particle swarm optimisation. The model was designed for long-term forecasting of both photovoltaic power generation and electrical load. For evaluation, researchers utilised smart meter consumption records alongside socio-economic and demographic data from Douala, paired with local meteorological records including solar irradiance, temperature, and humidity. Across standard performance metrics, the hybrid system achieved a root mean square error of 1.15, a mean absolute error of 0.75, and a correlation coefficient of 0.999. These results outperformed several alternative models, including recurrent neural networks, gated recurrent units, decision trees, and extreme gradient boosting, demonstrating effective joint prediction capabilities for smart grid operations.
Renewable energy sources like solar power fluctuate with the weather, making it challenging to balance electricity supply with consumer demand. By accurately predicting both solar generation and electrical consumption over the long term, grid operators can better plan infrastructure, prevent blackouts, and integrate clean energy more reliably into growing urban distribution networks.
This research provides an applied and tested forecasting tool that could benefit municipal utility providers, smart grid operators, and energy management firms. By utilising real-world smart meter and local weather records, the model demonstrates practical viability for grid planning. However, moving it into an operational commercial software environment would require integration with live utility telemetry systems and testing across different geographic regions.
AI-generated from the published abstract. Always read the original work before citing.
AbstractThe growing of the photovoltaic (PV) panel’s installation in the world and the intermittent nature of the climate conditions highlights the importance of power forecasting for smart grid integration. This work aims to study and implement existing Deep Learning (DL) methods used for PV power and electrical load forecasting. We then developed a novel hybrid model made of Feed-Forward Neural Network (FFNN), Long Short Term Memory (LSTM) and Multi-Objective Particle Swarm Optimization (MOPSO). In this work, electrical load forecasting is long-term and will consider smart meter data, socio-economic and demographic data. PV power generation forecasting is long-term by considering climatic data such as solar irradiance, temperature and humidity. Moreover, we implemented these deep learning methods on two datasets, the first one is made of electrical consumption data collected from smart meters installed at consumers in Douala. The second one is made of climate data collected at the climate management center in Douala. The performances of the models are evaluated using different error metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) and regression (R). The proposed hybrid model gives a RMSE, MAE and R of 1.15, 0.75 and 0.999 respectively. The results obtained show that the novel deep learning model is effective in the both electrical load prediction and PV power forecasting and outperforms other models such as FFNN, Recurrent Neural Network (RNN), Decision Tree (DT), Gated Recurrent Unit (GRU) and eXtreme Gradient Boosting (XGBoost).
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1080/23311916.2024.2340302
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
