Ahmed Abrar, Ali Safdar, Raza Ali, Hussain Ibrar, Bilal Ahmad, Fitriyani Norma Latif, Gu Yeonghyeon, Syafrudin Muhammad
Department of Software Engineering, University of Lahore, Lahore, Pakistan.
Faculty of Engineering & Information Technology, Shinawatra University, Bangtoey Samkhok, Pathum Thani, Thailand.
PLoS One. 2025 Jan 7;20(1):e0315668. doi: 10.1371/journal.pone.0315668. eCollection 2025.
Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. However, due to the constraints and costs associated with energy generation resources, it has become crucial for both energy generation companies and consumers to predict energy consumption well in advance. Forecasting energy needs through accurate predictions enables companies and customers to make informed decisions, enhancing the efficiency of both energy generation and consumption. In this context, energy generation companies and consumers seek a model capable of forecasting energy consumption both in the short term and the long term. Traditional models for energy prediction focus on either short-term or long-term accuracy, often failing to optimize both simultaneously. Therefore, this research proposes a novel hybrid model employing Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) to simultaneously predict both short-term and long-term residential energy consumption with enhanced accuracy measures. The proposed model is capable of capturing complex temporal and spatial features to predict short-term and long-term energy consumption. CNNs discover patterns in data, LSTM identifies long-term dependencies and sequential patterns and Bi-LSTM identifies complex temporal relations within the data. Experimental evaluations expressed that the proposed model outperformed with a minimum Mean Square Error (MSE) of 0.00035 and Mean Absolute Error (MAE) of 0.0057. Additionally, the proposed hybrid model is compared with existing state-of-the-art models, demonstrating its superior performance in both short-term and long-term energy consumption predictions.
能源对于每个国家的社会经济发展至关重要。这种发展导致能源消耗需求迅速增长。然而，由于与能源生产资源相关的限制和成本，对于能源生产公司和消费者来说，提前准确预测能源消耗变得至关重要。通过准确预测来预测能源需求，能够使公司和客户做出明智的决策，提高能源生产和消耗的效率。在这种背景下，能源生产公司和消费者寻求一种能够在短期和长期内预测能源消耗的模型。传统的能源预测模型要么侧重于短期准确性，要么侧重于长期准确性，往往无法同时对两者进行优化。因此，本研究提出了一种新颖的混合模型，该模型采用卷积神经网络（CNN）、长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM），以更高的准确性同时预测短期和长期居民能源消耗。所提出的模型能够捕捉复杂的时空特征，以预测短期和长期能源消耗。卷积神经网络可发现数据中的模式，长短期记忆网络可识别长期依赖性和序列模式，双向长短期记忆网络可识别数据中的复杂时间关系。实验评估表明，所提出的模型表现出色，最小均方误差（MSE）为0.00035，平均绝对误差（MAE）为0.0057。此外，将所提出的混合模型与现有的先进模型进行了比较，证明了其在短期和长期能源消耗预测方面的卓越性能。
