Jul 2026· De Computis· Vol 15· 0 citations· 23 references
Computer Science
TL;DR
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.
Abstract
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.
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
Accurate electricity load forecasting is essential for smart-grid scheduling, yet existing deep learning methods often struggle to capture multiple temporal scales efficiently. This paper proposes an adaptive multi-scale decomposition (AMD) framework comprising three modules. Multi-scale decomposable mixing extracts hierarchical temporal representations through downsampling and residual mixing. Dual dependency interaction captures temporal dependencies and variable coupling through parallel time and channel mixing. Adaptive multi-predictor synthesis combines scale-specific predictions using autocorrelation-based soft Top-K routing, emphasizing dominant scales while retaining contributions from other scales. Experiments on six electricity-domain benchmark datasets, including Electricity Consuming Load, Electricity Transformer Temperature (ETT), and Solar Energy, show that AMD achieves an average mean squared error (MSE) of 0.28 and mean absolute error of 0.33 for long-term forecasting, providing performance comparable to TimeMixer. AMD performs favorably on Solar Energy and long-horizon Electricity Consuming Load (ECL) tasks but is marginally outperformed by TimeMixer on ETTh2, where trend-dominated patterns favor fixed multi-scale weighting. With 2.3 million parameters, AMD requires 45.2 s per training epoch and 12.5 ms per inference, placing it within the same efficiency range as TimeMixer and below Transformer-based baselines. Ablation, sensitivity, and visualization analyses confirm the contributions of all three modules and reveal that daily and hourly scales account for 35.8% and 28.5% of scale attribution, respectively. Overall, AMD offers a competitive accuracy-efficiency trade-off for electricity-load benchmark forecasting under mixed or shifting temporal periodicities. Its applicability to real-world smart-grid operations requires further validation.
Gao-Jian Wang· Energy Exploration & Exp...· 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
Many current prediction methods used in power system load forecasting are adversely affected by an imbalance of feature distributions at different time scales, yielding constrained prediction accuracy and elevated error rates. This paper introduces a multiscale prediction method integrating a multiscale attention convolutional network with a long short-term memory (LSTM) network. First, historical charging-load data from charging stations are obtained and structured into distinct temporal segments for multiscale convolutional feature learning. The multiscale attention convolutional network employs parallel convolutional branches with varying dilation depths to process the input sequence, integrating the outputs via feature concatenation and channel weighting to form unified conceptual representations. An LSTM network models the multilevel time-series data derived from the various convolutional filters, enabling predictions across multiple time scales. The attention mechanism assigns weighted importance scores to the fused features based on temporal relevance prior to sequence modeling, improving prediction stability across various load periods for electric vehicle charging loads. The multi-time-scale prediction experiment demonstrates consistent accuracy at the medium and long time scales, where the mean absolute percentage error across all periods does not exceed 5%, and the coefficient of determination remains stable above 0.9. Through ablation experiments, this paper validates that the integrated multiscale attention convolutional network and LSTM framework yield a mean absolute error of 8.8 kW and a root mean square error of 15.5 kW during peak periods, outperforming single-scale architectures. Accordingly, the model provides an accurate, quantifiable foundation for power system load forecasting.
Haoyang Xie, Xi Chen· Mechanics & Industry· 0 citations
With the advancement of smart grid and electricity market reform, electricity billing systems need to accurately integrate multi-source heterogeneous data (such as power load, meteorological information and user behavior) to achieve dynamic billing. However, existing models are difficult to balance between spatiotemporal feature extraction and dynamic load fluctuation capture, resulting in prediction results that are susceptible to data heterogeneity interference and delayed response to sudden load changes (such as extreme weather or holiday electricity consumption surges), affecting the realtime and fairness of electricity billing. To address the above problems, this paper proposes an improved CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) joint model, which achieves high-precision time series prediction and robustness improvement through a multi-source heterogeneous data preprocessing module, a dynamic spatiotemporal feature fusion mechanism and a nonlinear load fluctuation prediction layer. Experimental results show that the improved model outperforms traditional ResNet (Residual Network), Transformer and basic CNN-LSTM models in terms of MAE (Mean Absolute Error) (0.85), RMSE (Root Mean Square Error) (1.10) and R² (0.92), and has low latency (82 ms) and high generalization ability. The median MAPE is as low as 4.0%-4.5% in the cold zone scenario of industrial users. This study provides a technical solution for the electricity billing system that takes into account both prediction accuracy and efficiency.
Xingyan Wang, Liangbin Dong, Xiao-Yue Huang et al.· International Conference on...· 0 citations
Reliable short-term prediction of electricity consumption is essential for dispatching power grids and incorporating renewable sources. This work presents an interpretable forecasting system that merges heterogeneous data streams with an attention-augmented PatchTST backbone. Real-world meteorological records from NOAA are combined with electricity demand data, and we design time-adaptive correlation descriptors alongside a binary extreme-weather flag. A one-dimensional Coordinate Attention (CA) mechanism, originally developed for image analysis, is integrated into the PatchTST encoder to enable channel-wise reweighting. Using the UCI Electricity Load dataset for customer MT_329 (2011-2012), our CA-PatchTST attains a MAE of 16.46, surpassing LSTM (18.40) and the unmodified PatchTST (17.59). Ablation tests indicate that incorporating meteorological information is the primary driver of accuracy gain, while the CA module contributes moderate improvement and, crucially, offers visualizable attention maps that clarify the model's behavior under severe weather. The fusion of real NOAA observations lifts forecasting performance by over 50% relative to load-only models. The dynamic correlation features effectively capture seasonal reversals in load–weather dependency across months. Overall, the proposed approach achieves competitive results (MAE 16.46) on the benchmark.
Bohan Zhang· Applied and Computational En...· 0 citations
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