Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-6· 0 citations· 21 references
Abstract
Accurate short-term load forecasting is essential for efficient smart grid operation and demand-side energy management. Traditional statistical models often struggle to capture nonlinear and irregular residential electricity consumption patterns. This paper proposes a hybrid deep learning architecture combining Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM) networks for submeterlevel load forecasting. The model uses dilated causal convolutions to extract short-term temporal features, followed by LSTM units to capture long-term dependencies in energy consumption data. Data preprocessing includes normalization and slidingwindow sequence generation to convert historical load data into supervised learning samples. The model is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) on both synthetically generated residential load profiles and the real-world UCI Household Electric Power Consumption dataset. The Hybrid TCN-LSTM model achieves an average RMSE of 0.0428 and MAE of 0.0317 on the synthetic dataset across 250 consumers and RMSE of 0.4551 kW with MAE of 0.2842 kW on the UCI dataset. Results demonstrate that the hybrid architecture effectively captures short-term fluctuations and longterm temporal dependencies, enabling reliable load forecasting for smart grid applications.
A framework for a comparative evaluation of four representative forecasting methods: the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, Extreme Gradient Boosting, the Long Short-Term Memory (LSTM) neural network, and a hybrid Variational Mode Decomposition–LSTM (VMD-LSTM) model is proposed.
Pratiman Patel, Prajwal Pal· International Journal for Re...· 0 citations
Accurate forecasting of power consumption is critical for efficient energy management, grid stability, and cost reduction. This study explores the application of advanced machine learning models to predict short-term and long-term power usage patterns. By leveraging historical consumption data alongside relevant external factors such as weather conditions, time of day, and economic indicators, the proposed approach employs algorithms including Random Forest, Support Vector Machines, and Deep Learning networks. The models are trained and validated on real-world datasets to evaluate their predictive accuracy and robustness. Results demonstrate that machine learning techniques significantly improve forecasting precision compared to traditional statistical methods. This enables smarter energy distribution, better demand response strategies, and supports the integration of renewable energy sources, thereby contributing to sustainable power system operation. Traditional statistical methods often fall short in capturing the complex, non-linear patterns of modern electricity usage. This project explores advanced machine learning techniques such as Random Forest, Support Vector Machines, and LSTM networks to predict both short-term and long- term electricity demand. By leveraging historical data, weather conditions, time-based factors, and user behavior, these models demonstrate superior forecasting performance compared to conventional methods. The study shows that machine learning enables smarter load balancing, better integration of renewable energy, and improved decision-making in power distribution systems. The proposed approach supports the development of intelligent, sustainable, and data-driven energy.
Keywords: Power Consumption Forecasting, Machine Learning, Random Forest, LSTM, Smart Grid, Energy Management, Electricity Demand Prediction, Time Series Forecasting.
M. Tarani, Tothadi Sowjanya· International Scientific Jou...· 0 citations
Electricity load forecasting is essential for balancing energy supply and demand, reducing energy waste, and maintaining power grid stability. Accurate forecasts enable power utilities to optimize energy dispatch and mitigate the risk of supply shortages or outages. However, conventional forecasting methods often struggle to capture highly nonlinear local fluctuations in electricity consumption and long-term temporal dependencies. To address these challenges, this study proposes MSCNN-ResLSTM, a hybrid model for multi-step electricity load forecasting. The proposed model integrates Multi-Scale Convolutional Neural Networks (MSCNNs) to extract local time-series features at multiple temporal scales, residual blocks (ResBlocks) to enhance feature representation through residual connections, and Long Short-Term Memory (LSTM) networks to model long-range temporal dependencies. To comprehensively evaluate its effectiveness, a cross-paradigm experimental framework is established in which MSCNN-ResLSTM is compared with seven representative benchmark models from three methodological categories: traditional machine learning (Extreme Gradient Boosting-XGBoost), classical recurrent and convolutional neural networks (LSTM, Temporal Convolutional Network-TCN, CNN-LSTM, MSCNN-LSTM, and Direct LSTM (Seq2Seq)), and self-attention-based architectures (Transformer). 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.
Accurate short-term load forecasting (STLF) is essential for modern grid operations, enabling efficient scheduling, demand response, and renewable energy integration. This paper presents a systematic comparison of five forecasting architectures applied to a large dataset of 98 residential homes, with 1-minute and 15-minute smart meter readings spanning 2015-2023. The models include an XGBoost pipeline with extensive feature engineering, a tuned CatBoost implementation, a feedforward neural network with multi-output regression, a multi-scale convolutional Kolmogorov-Arnold network (MCKAN), and a long short-term memory (LSTM) network with a 7-day lookback. All models are trained globally, pooling data across homes while incorporating home-specific categorical variables, including an assignment to a simulated microgrid topology with nine kiosks and three phases. Hyperparameter optimization is performed using Optuna and Keras Tuner. CatBoost achieves the lowest test MAE across all horizons, from 0.4089 (15-minute) to 0.7419 (30-day), outperforming XGBoost by 4-10% and deep learning models by larger margins. The findings support Sustainable Development Goals 7, 9, and 13 and provide actionable insights for energy management, particularly in the South African context of load shedding and grid decarbonization.
Mukovhe Ratshitanga, Pfano Nemakonde, Komla A. Folly et al.· 2026 6th 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
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.
Wenjun Guo· Applied and Computational En...· 0 citations