A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction

Li, Kangji; Xie, Xianming; Xue, Wenping; Dai, Xiaoli; Xu, Chunxiao; Yang, Xinyun

doi:10.1016/j.enbuild.2018.06.017

Cited by 97 publications

(37 citation statements)

References 25 publications

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“…Kim et al [28] used a hybrid approach combining a CNN with LSTM for short-term ECP. Likewise, Li et al [29] developed an evolutionary algorithm called teaching-learning-based optimisation (TLBO) to predict short-term residential energy consumption. They further modified the algorithm by combining it with an ANN to give further improvement.…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks

et al. 2020

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Excessive power consumption (PC) and demand for power is increasing on a daily basis, due to advancements in technology, the rise in electricity-dependent machinery, and the growth of the human population. It has become necessary to predict PC in order to improve power management and cooperation between the energy used in a building and the power grid. State-of-the-art energy consumption prediction (ECP) methods are limited in terms of predicting the energy effectively, due to various challenges such as weather conditions and the dynamic behaviour of occupants. Thus, to overcome the drawbacks of these methods, we present an intelligent hybrid technique that combines a convolutional neural network (CNN) with a multi-layer bi-directional long-short term memory (M-BLSTM) method using three steps. When applied to short-term power ECP, this approach helps to provide efficient power management i.e. it can assist the supplier to produce the optimum amount of power. The first step in our proposed method integrates the pre-processing and data organisation mechanisms to refine the data and remove abnormalities. The second step employs a deep learning network, where the sequence of refined data is fed into the CNN via the M-BLSTM network to learn the sequence pattern effectively. The third step generates the ECP/PC by comparing actual and predicted data series and evaluates the prediction using error metrics. The proposed method achieves better prediction results than existing techniques, thus demonstrating its effectiveness. Furthermore, it achieved the smallest value of the Mean Square Error (MSE) and Root Mean Square Error (RMSE) for individual household dataset using 10-fold cross validation (CV) and a hold-out (CV) method. INDEX TERMS Artificial intelligence, deep learning, power consumption, CNN, bi-directional LSTM, and short-term energy consumption.

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Section: B Deep Learning Methodsmentioning

confidence: 99%

Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks

et al. 2020

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“…Several algorithms were combined to form ensemble learning models. Li et al [15] proposed teaching-learning based optimization with artificial neural network for hourly energy prediction. Khairalla et al [16] investigated Stacking Multi-Learning Ensemble (SMLE) model and combined Support Vector Regression (SVR), neural network, and linear regression learners.…”

Section: Related Workmentioning

confidence: 99%

Similarity-Based Chained Transfer Learning for Energy Forecasting With Big Data

2019

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Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building or a single aggregated load to predict future consumption for that same building or aggregated load. With hundreds of thousands of meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Similarity-Based Chained Transfer Learning (SBCTL), an approach for building neural network-based models for many meters by taking advantage of already trained models through transfer learning. The first model is trained in a traditional way whereas all other models transfer knowledge from the existing models in a chain-like manner according to similarities between energy consumption profiles. A Recurrent Neural Network (RNN) was used as the base forecasting model, two initialization techniques were considered, and different similarity measures were explored. The experiments show that SBCTL achieves accuracy comparable to traditional ML training while taking only a fraction of time.INDEX TERMS Big data, deep learning, energy forecasting, gated recurrent units, recurrent neural network, smart meters, transfer learning.

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“…However, the SVM has a slow speed of training large-scale samples [19,20]. As a "black-box" that relies on data and prior knowledge, the traditional ANN can fit complex nonlinear relationships, whereas the traditional ANN also has defects of over-fitting and easy to fall into local optimum [21,22]. In addition, the above methods only account for the impact of the environmental factors on the current loads, ignoring the role of past events.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning

2019

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Combined cooling, heating, and power (CCHP) systems is a distributed energy system that uses the power station or heat engine to generate electricity and useful heat simultaneously. Due to its wide range of advantages including efficiency, ecological, and financial, the CCHP will be the main direction of the integrated system. The accurate prediction of heating, gas, and electrical loads plays an essential role in energy management in CCHP systems. This paper combined long short-term memory (LSTM) network and convolutional neural network (CNN) to design a novel hybrid neural network for short-term loads forecasting considering their correlation. Pearson correlation coefficient will be utilized to measure the temporal correlation between current load and historical loads, and analyze the coupling between heating, gas and electrical loads. The dropout technique is proposed to solve the over-fitting of the network due to the lack of data diversity and network parameter redundancy. The case study shows that considering the coupling between heating, gas and electrical loads can effectively improve the forecasting accuracy, the performance of the proposed approach is better than that of the traditional methods.

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A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction

Cited by 97 publications

References 25 publications

Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks

Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks

Similarity-Based Chained Transfer Learning for Energy Forecasting With Big Data

Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning

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