Development of Temporal-Spatial Long Short-Term Memory with Attention Mechanism for Electricity Short-Term Load Forecasting in Bauchi Metropolis

References

[1] Klyuev RV, Morgoev ID, Morgoeva AD, Gavrina OA, Martyushev NV, Efremenkov EA, et al. Methods of forecasting electric energy consumption: A literature review. Energies. 2022;15(23):8919.

[2] Dewangan F, Abdelaziz AY, Biswal M. Load forecasting models in smart grid using smart meter information: A review. Energies. 2023;16(3):1404.

[3] Gross G, Galiana FD. Short-term load forecasting. Proc. IEEE. 1987;75(12):1558–1573.

[4] Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid. 2019;10(1):841–851.

[5] Saffari M, Khodayar M. Spatiotemporal deep learning for power system applications: A survey. IEEE Access. 2024;12:93623–93657.

[6] Martinez-Zapata K, Ospina-Acero D, Granada-Torres J, Muñoz-Galeano N, Gaviria-Gómez N, Botero-Vega J, et al. Data-driven load forecasting in microgrids: Integrating external factors for efficient control and decision-making. Energies. 2026;19:555.

[7] Peteleaza D, Matei A, Sorostinean R, Gellert A. Internet of Things electricity consumption forecasting for sustainable smart cities using machine learning methods. Internet of Things. 2024;27:101322.

[8] Liu Y, Li Y, Li G, Lin Y, Wang R, Fan Y. Review of multiple load forecasting method for integrated energy system. Front. Energy Res. 2023;11:1296800.

[9] Ullah K, Ahsan M, Hasanat SM, Haris M, Yousaf H, Raza SF, et al. Short-term load forecasting: A comprehensive review and simulation study with CNN-LSTM hybrids approach. IEEE Access. 2024;12:111858–111881.

[10] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–1780.

[11] da Silva DG, de Moura Meneses AA. Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction. Energy Rep. 2023;10:3315–3334.

[12] Lv Y, Wang L, Long D, Hu Q, Hu Z. Multi-area short-term load forecasting based on spatiotemporal graph neural network. Eng. Appl. Artif. Intell. 2024;138:109398.

[13] Linfei Y, Xie J. Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems. Appl. Energy. 2020;283:116328.

[14] Dou X, Yang R, Dou Z, Zhang C, Xu C, Li J. A load forecasting model based on spatiotemporal partitioning and cross-regional attention collaboration. Sustainability. ;17(18):2025.

[15] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. In: Advances in Neural Information Processing Systems (NeurIPS). 2017. p. 5998–6008.

[16] Liao W, Ruan J, Xie Y, Wang Q, Li J, Wang R, et al. Deep learning time pattern attention mechanism-based short-term load forecasting method. Front. Energy Res. 2023;11:1227979.

[17] Aouad M, Hajj H, Shaban K, Jabr R, El-Hajj W. A CNN-Sequence-to-Sequence network with attention for residential short-term load forecasting. Electr. Power Syst. Res. 2022;211:108152.

[18] Guo W, Liu S, Weng L, Liang X. Power grid load forecasting using a CNN-LSTM network based on a multi-modal attention mechanism. Appl. Sci. 2025;15(5):2435.

[19] Wen X, Li W. Time series prediction based on LSTM-Attention-LSTM model. IEEE Access. 2023;11:48322–48331.

[20] Feng Y, Zhu J, Qiu P. Short-term power load forecasting based on TCN-BiLSTM-Attention and multi-feature fusion. Arab. J. Sci. Eng. 2024;50(7):1–15.

[21] Lu S, Bao T. Short-term electricity load forecasting based on NeuralProphet and CNN-LSTM. IEEE Access. 2024;12:76870–76879.

[22] Li J, Li J, Li J, Zhang G. Bayesian-optimized GCN-BiLSTM-Adaboost model for power-load forecasting. Electronics. 2025;14(16):3332.

[23] Wu M, Feng W, Li X, Liu Y, Cao C. Short-term power load forecasting using an improved model integrating GCN and Transformer. Appl. Sci. 2025;15(13):7003.

[24] Wen X, Liao J, Niu Q, Shen N, Bao Y. Deep learning-driven hybrid model for short-term load forecasting and smart grid information management. Sci. Rep. 2024;14(1):63262.

[25] Liang D, Wu J, Xu X, Xiu C, Wang Y. Enhancing grid reliability through Transformer-LSTM model in smart grids. In: Proc. SPIE. 2025.

[26] Zang H, Xu R, Cheng L, Ding T, Liu L, Wei Z, et al. Residential load forecasting based on LSTM fusing self-attention mechanism with pooling. Energy. 2021;229:120682.

[27] Lin N, Wen X, Wu J, Huang X, Wen H. Intelligent monitoring and anomaly detection for power service processes based on spatiotemporal attention mechanism. Sci. Rep. 2026;16(1):42189.

@article{1724757,
      author = {Ahmad Adamu Zakari, Ganiyu A. Bakare, Mustapha A. Lawal},
      title = {Development of Temporal-Spatial Long Short-Term Memory with Attention Mechanism for Electricity Short-Term Load Forecasting in Bauchi Metropolis},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {1018-1029},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724757.pdf},
      abstract = {Electricity short-term load forecasting (STLF) represents a critical operational imperative for modern power system economic dispatch, spinning reserve allocation, and grid stability. Within distribution networks, electricity demand exhibits intricate non-linear temporal dynamics alongside pronounced spatial correlations across interconnected feeder nodes. Conventional Long Short-Term Memory (LSTM) recurrent networks effectively model temporal sequences but fail to capture cross-feeder spatial dependencies and encounter representational bottlenecks over extended look-back horizons. This paper proposes a novel Temporal-Spatial Long Short-Term Memory architecture integrated with dual-stage attention mechanisms (TS-LSTM-Attn) for one-hour-ahead multi-output load forecasting across the eight 33 kV distribution feeders of Bauchi Metropolis, Nigeria (GRA/Palace, Works, Yankari, Nabordo, Ningi, Dass, Dabo Farm, and Gubi Dam). The architecture integrates an LSTM temporal recurrent encoder, four-head spatial self-attention across feeder nodes, residual connections with feed-forward layer normalization, temporal sequence attention over a 168-hour historical look-back window, and feeder-specific embeddings. Real-world hourly electricity load telemetry spanning 2023 to 2026 was synchronized with hourly meteorological regressors obtained from the Nigerian Meteorological Agency (NiMet) and engineered calendar variables. Evaluated under strict leakage-free chronological partitioning, the proposed TS-LSTM-Attn model achieved a mean feeder-level Mean Absolute Error (MAE) of 0.8240 kW across the eight distribution feeders, outperforming the conventional standard LSTM (1.1232 kW) by 26.63% and surpassing the competitive persistence forecasting benchmark (0.8394 kW) by 1.83%. At the aggregate system level, the proposed model attained a Total Bauchi MAE of 3.9444 kW compared to 4.3532 kW for the standard LSTM (a 9.39% improvement) and 4.0087 kW for persistence. Interpretability analysis revealed that the temporal attention layer concentrates over 92.32% of its representational weight on the immediate past three hours (t-1: 67.87%, t-2: 18.31%, t-3: 6.14%), while the learned spatial attention matrix reflects distributed multi-feeder interaction (~0.12–0.13) governed by shared 132/33 kV substation busbar dynamics. The study provides empirical proof that fusing spatiotemporal feature learning with dual-stage attention substantially enhances both forecasting accuracy and operational interpretability for utility dispatchers in developing distribution grids.},
      keywords = {Electricity short-term load forecasting; Long Short-Term Memory (LSTM); Multi-head spatial self-attention; Temporal sequence attention; 33 kV distribution network; Multi-output regression; Bauchi power grid},
      month = {November},
  }