Abstract
Forecasting of energy consumption in Smart Buildings (SB) and using the extracted information to plan and operate power generation are crucial elements of the Smart Grid (SG) energy management. Prediction of electrical loads and scheduling the generation resources to match the demand enable the utility to mitigate the energy generation cost. Different methodologies have been employed to predict energy consumption at different levels of distribution and transmission systems. In this paper, a novel hybrid deep learning model is proposed to predict energy consumption in smart buildings. The proposed framework consists of two stages, namely, data cleaning, and model building. The data cleaning phase applies pre-processing techniques to the raw data and adds additional features of lag values. In the model-building phase, the hybrid model is trained on the processed data. The hybrid deep learning (DL) model is based on the stacking of fully connected layers, and unidirectional Long Short Term Memory (LSTMs) on bi-directional LSTMs. The proposed model is designed to capture the temporal dependencies of energy consumption on dependent features and to be effective in terms of computational complexity, training time, and forecasting accuracy. The proposed model is evaluated on two benchmark energy consumption datasets yielding superior performance in terms of accuracy when compared with widely used hybrid models such as Convolutional (Conv) Neural Network-LSTM, ConvLSTM, LSTM encoder-decoder model, stacking models, etc. A mean absolute percentage error (MAPE) of 2.00% for case study 1 and a MAPE of 3.71% for case study 2 is obtained for the proposed forecasting DL model in comparison with LSTM-based models that yielded 7.80% MAPE and 5.099% MAPE for two datasets respectively. The proposed model has also been applied for multi-step week-ahead daily forecasting with an improvement of 8.368% and 20.99% in MAPE against the LSTM-based model for the utilized energy consumption datasets respectively.
Highlights
Energy consumption in buildings is one of the significant contributors to energy efficiency programs worldwide [1]
EXPERIMENTAL RESULTS several experiments were performed with a goal to obtain high accuracy energy forecasting in smart buildings using the proposed HSBUFC model and to compare the performance of the proposed model with that of other baseline models and widely employed hybrid deep learning models
The experimental results have been obtained after the execution on a supercomputer with the specifications of Nodes: 1, Cores: 8, Intel Central Processing Unit (CPU) with clock rate: 2.4 GHz, Random Access Memory (RAM): 256 GB, Network speed: 40-100 Gpbs, and programming environment: python. 80% of the data was used for training, and 20% of the data was used as a testing dataset
Summary
Energy consumption in buildings is one of the significant contributors to energy efficiency programs worldwide [1]. Multiple efficient and accurate energy consumption forecasting models were developed based on ensemble models, extreme learning machines, LSTMs, deep neural networks, and dimensionality reduction techniques [14], [21], [22]. In [23], the authors developed hybrid sequential learning based on the deep learning model Their solution utilizes CNN in the first phase to extract the features from the energy consumption dataset and uses Gated Recurrent Unit (GRU) in the second phase to utilize its effective gated structure to make predictions. In addition to the ability to utilize information from the recurrent connections to the outputs of previous time steps, LSTMs have memory cells to accumulate steps over prediction sequences enabling them to perform better with long-term dependency tasks such as energy forecasting. Where H represents the hidden layer function, yt represents the input sequence
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