Department of Electrical Engineering, Mirpur University of Science & Technology, Mirpur 10250, Azad Kashmir, Pakistan.
Department of Electrical Engineering, University of Azad Jammu and Kashmir, Chehla Campus, Muzaffarabad 13100, Azad Kashmir, Pakistan.
Math Biosci Eng. 2020 Dec 4;18(1):400-425. doi: 10.3934/mbe.2021022.
An efficient management and better scheduling by the power companies are of great significance for accurate electrical load forecasting. There exists a high level of uncertainties in the load time series, which is challenging to make the accurate short-term load forecast (STLF), medium-term load forecast (MTLF), and long-term load forecast (LTLF). To extract the local trends and to capture the same patterns of short, and medium forecasting time series, we proposed long short-term memory (LSTM), Multilayer perceptron, and convolutional neural network (CNN) to learn the relationship in the time series. These models are proposed to improve the forecasting accuracy. The models were tested based on the real-world case by conducting detailed experiments to validate their stability and practicality. The performance was measured in terms of squared error, Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). To predict the next 24 hours ahead load forecasting, the lowest prediction error was obtained using LSTM with R2 (0.5160), MLP with MAPE (4.97), MAE (104.33) and RMSE (133.92). To predict the next 72 hours ahead of load forecasting, the lowest prediction error was obtained using LSTM with R2 (0.7153), MPL with MAPE (7.04), MAE (125.92), RMSE (188.33). Likewise, to predict the next one week ahead load forecasting, the lowest error was obtained using CNN with R2 (0.7616), MLP with MAPE (6.162), MAE (103.156), RMSE (150.81). Moreover, to predict the next one-month load forecasting, the lowest prediction error was obtained using CNN with R2 (0.820), MLP with MAPE (5.18), LSTM with MAE (75.12) and RMSE (109.197). The results reveal that proposed methods achieved better and stable performance for predicting the short, and medium-term load forecasting. The findings of the STLF indicate that the proposed model can be better implemented for local system planning and dispatch, while it will be more efficient for MTLF in better scheduling and maintenance operations.
对于准确的电力负荷预测，电力公司进行有效的管理和更好的调度具有重要意义。在负荷时间序列中存在高水平的不确定性，这使得准确的短期负荷预测（STLF）、中期负荷预测（MTLF）和长期负荷预测（LTLF）变得具有挑战性。为了提取局部趋势并捕捉短期和中期预测时间序列的相同模式，我们提出了长短期记忆（LSTM）、多层感知器和卷积神经网络（CNN）来学习时间序列中的关系。这些模型的提出是为了提高预测精度。通过进行详细的实验来验证其稳定性和实用性，基于实际案例对模型进行了测试。性能是根据均方误差、均方根误差（RMSE）、平均绝对百分比误差（MAPE）和平均绝对误差（MAE）来衡量的。为了预测未来 24 小时的负荷预测，使用 LSTM 获得的预测误差最低，R2 为 0.5160、MLP 的 MAPE 为 4.97、MAE 为 104.33 和 RMSE 为 133.92。为了预测未来 72 小时的负荷预测，使用 LSTM 获得的预测误差最低，R2 为 0.7153、MLP 的 MAPE 为 7.04、MAE 为 125.92 和 RMSE 为 188.33。同样，为了预测未来一周的负荷预测，使用 CNN 获得的误差最低，R2 为 0.7616、MLP 的 MAPE 为 6.162、MAE 为 103.156 和 RMSE 为 150.81。此外，为了预测未来一个月的负荷预测，使用 CNN 获得的预测误差最低，R2 为 0.820、MLP 的 MAPE 为 5.18、LSTM 的 MAE 为 75.12 和 RMSE 为 109.197。结果表明，所提出的方法在预测短期和中期负荷预测方面具有更好和更稳定的性能。短期负荷预测的结果表明，所提出的模型可以更好地用于本地系统规划和调度，而在更好的调度和维护操作中，中期负荷预测将更加有效。
