Aug 2026· Applied and Computational Engineering· Vol 255, pp. 69-75· 0 citations
TL;DR
This study develops a PyTorch-based LSTM model for short-term electricity load forecasting, using South Australian electricity demand data from 2024, and shows that the LSTM model can follow the main movement of electricity demand, but the prediction curve is smoother than the actual curve and some short-term fluctuations remain difficult to capture.
Abstract
Accurate electricity load forecasting is important for power system operation, energy planning and demand management. With the increasing use of data-driven methods, deep learning models have become useful tools for analysing complex electricity demand patterns. This study develops a PyTorch-based LSTM model for short-term electricity load forecasting, using South Australian electricity demand data from 2024. The original dataset consists of chronological five-minute demand records, and total electricity demand is selected as the target variable. Missing values were checked, the demand series was normalised using training-set statistics, and supervised learning samples were generated by a sliding window method. In the proposed model, the previous 24 five-minute demand observations are used to predict the next demand value. The dataset is divided into training, validation and testing subsets in chronological order. The model is trained using mean squared error loss and the Adam optimiser with a step learning-rate schedule. The results show that the LSTM model can follow the main movement of electricity demand, but the prediction curve is smoother than the actual curve and some short-term fluctuations remain difficult to capture. This limitation is mainly caused by the use of a simple univariate input, because weather, calendar, electricity price and renewable generation variables are not included. Future work should introduce multivariate features, compare LSTM against benchmark models such as GRU and ARIMA, and test longer input windows to further improve forecasting accuracy.
Accurate short-term load forecasting is essential for reliable and efficient power system operation, yet its accuracy is influenced by prediction horizon and load dynamics. This study evaluates Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer models under baseline and incremental learning frameworks using Supervisory Control and Data Acquisition (SCADA) load data from the West Kalimantan power system. The models are trained on 30 -minute resolution data from January 2018 to July 2025 and evaluated in a real-time streaming setting from August 2025 to March 2026, where predictions are generated sequentially and model updates are performed only after new observations become available, without access to future data. The evaluation focuses on a 24 -hour forecasting horizon, with incremental learning implemented using update intervals of 6h, 12h, 24h, and 48h. Results show that forecasting errors increase with longer prediction horizons across all models. The Transformer follows this trend but exhibits lower performance than recurrent-based models, particularly at shorter horizons. Among the evaluated models, GRU achieves the best overall forecasting performance, reducing MAE from 15.67 to 15.30 (2.3%) at the 24-hour update interval, with RMSE and MAPE of 20.90 and 3.45%, respectively. The Diebold-Mariano (DM) test confirms statistically significant differences between baseline and incremental forecasting performance $(\boldsymbol{p}<\mathbf{0. 0 0 1})$. Overall, the performance gain from incremental learning is modest under relatively stable load conditions, indicating that its effectiveness depends on data variability.
Arif Rahman Hakim, D. Purwanto, I. M. Y. Negara· International Seminar on Int...· 0 citations
Overall, the study demonstrates that recurrent deep learning architectures provide reliable and operationally relevant solutions for electricity consumption forecasting in highly structured energy demand series.
Séna Apeke, Yao Bokovi, K. Gbafa et al.· Science Journal of Energy En...· 0 citations
Experimental results show that MSCNN-ResLSTM achieves higher forecasting accuracy and greater stability across the full 24-step prediction horizon, consistently outperforming all competing baselines while effectively suppressing recursive error propagation.
Yu-Hang Zhang, Yi-Ting Zhao, Yu-Jing Meng et al.· De Computis· 0 citations
Short-term temperature prediction plays a vital role in agriculture, water resource management, energy planning and climate change monitoring. Nevertheless, traditional statistical methods do not account for the nonlinearities in weather patterns, whereas deep learning models may not consider the linear time series characteristics. To solve this problem, the present study proposes a hybrid Auto-Regressive Integrated Moving Average-Long Short Term Memory (ARIMA-LSTM) model for short-term temperature prediction. The ARIMA model will be used in the proposed framework to identify the linear and seasonal components of temperature patterns. The errors produced by the ARIMA model will be further analyzed by means of an LSTM neural network that would model the non-linear time series behavior. The model is tested on the Tamil Nadu and Puducherry Weather Dataset (2022), consisting of roughly 1.8 million weather data sets, including variables like temperature of the air, humidity level, wind speed, and pressure. The evaluation criteria include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The experimental findings reveal that the developed model performs better in terms of accuracy than traditional deep learning models, thus providing an effective and reliable tool for predicting temperature in short intervals.
Akkala Yugandhara Reddy, D.Sravani, M.Vaishnavi et al.· 2026 4th International Confe...· 0 citations
For smart grid power system planning and operation, short-term load forecasting is crucial. Important decisions including determining system safety, scheduling fuel, economically dispatching electricity, and selling energy can be aided by accurate day-ahead estimates. However, due to its reliance on external variables like weather, the process is intricate and computationally intensive. In order to address this issue, the paper proposes an LSTGR-based architecture that systematically enhances STLF in Smart Grids. The input characteristics are first scaled correctly using data normalisation. A hybrid feature selection method combining XGB and RF is utilised to determine the most essential features. Afterwards, RFE is employed to eliminate superfluous attributes. To facilitate learning, a hybrid deep learning model is trained using the updated dataset. This model combines LSTM and GRU. Using assessment criteria such as MAPE, MAE, MSE, and RMSE, the results demonstrate that the LSTGR model outperforms other models. With an RMSE of only 1.8%, the model clearly excels at producing accurate predictions. All things considered, the model successfully improves the reliability of forecasts while being computationally efficient. Because of this, it is an excellent option for smart grid applications in the actual world.
L. Jayavani, Banoth Ashwini, Kolkur Swabhavika et al.· 2026 7th International Confe...· 0 citations
Increasing air pollution in urban areas, particularly in DKI Jakarta, has made a reliable air quality prediction system increasingly essential for environmental control and public health risk management. The Air Quality Index (AQI) exhibits complex and fluctuating patterns, requiring forecasting methods capable of capturing both linear and non-linear. This study aims to conduct a comparative analysis of statistical, deep learning, and hybrid models for AQI forecasting using daily AQI data from Jakarta during the 2023–2025 period. The dataset includes polutant parameters such as , , , , , and CO. The proposed models consist of Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM), and a hybrid ARIMA-LSTM model. The research methodology includes data preprocessing, normalization using Min-Max Scaling, sequence generation using the sliding window approach, model training, and evaluation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The experimental results show that the CNN-LSTM model achieved the best forecasting performance with MAE, RMSE, and MAPE values of 4.54, 6.03, and 13.83%, respectively, followed closely by the LSTM model. Meanwhile, the ARIMA model produced the lowest performance, and the hybrid ARIMA-LSTM model did not outperform the standalone deep learning models. These findings indicate that deep learning approaches, particularly CNN-LSTM, are more effective in capturing the complex dynamics of urban air pollution data and have strong potential to support air quality forecasting and pollution control systems in Jakarta.
Mushliha, Nisrinah· IDEALIS : InDonEsiA journaL...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
