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.

Household-Level Energy Forecasting in Smart Buildings Using a Novel Hybrid Deep Learning Model

Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building

Smart cities are an innovative concept for managing metropolitan areas to increase their residents’ sustainability and quality of life. This article examines the management and evolution of energy generation, various storage systems and the applications they serve, and infrastructure technology’s current condition and future prospects. Additionally, the study also examines energy-related construction and transportation systems and technologies. The Smart Cities Energy Prediction Task Force predicts electrical usage using STLF, SVM, and e-learning machines. To keep a system working well throughout the year, fossil fuels must be utilised as a backup energy source. Technologies can only benefit if integrated into the city’s infrastructure. By 2050, it is anticipated that the global population will surpass 10 billion, with most people settling in metropolitan regions. Between 2020 and 2027, the global market for smart energy is anticipated to expand by 27.1% annually, from USD 122.2 billion in 2020 to USD 652 billion in 2026. In 2020, Europe will account for 31.8 per cent of total smart energy product sales. China’s GDP is projected to grow by 33.0 per cent annually, reaching USD 176.1 billion by the conclusion of the analysis period. Consequently, smart cities are expanding and blooming worldwide, yet there are no permanent standards.

/pdf/energy-and-sustainable-development-in-smart-cities-an-2eyeddgw.pdf

Energy and Sustainable Development in Smart Cities: An Overview

Load forecasting is critical for power system operation and market planning. With the increased penetration of renewable energy and the massive consumption of electric energy, improving load forecasting accuracy has become a difficult task. Recently, it was demonstrated that deep learning models perform well for short-term load forecasting (STLF). However, prior research has demonstrated that the hybrid deep learning model outperforms the single model. We propose a hybrid neural network in this article that combines elements of a convolutional neural network (1D-CNN) and a long short memory network (LSTM) in novel ways. Multiple independent 1D-CNNs are used to extract load, calendar, and weather features from the proposed hybrid model, while LSTM is used to learn time patterns. This architecture is referred to as a CNN-LSTM network with multiple heads (MCNN-LSTM). To demonstrate the proposed hybrid deep learning model’s superior performance, the proposed method is applied to Ireland’s load data for single-step and multi-step load forecasting. In comparison to the widely used CNN-LSTM hybrid model, the proposed model improved single-step prediction by 16.73% and 24-step load prediction by 20.33%. Additionally, we use the Maine dataset to verify the proposed model’s generalizability.

/pdf/multi-convolution-feature-extraction-and-recurrent-neural-jcqvbo8zpw.pdf

Multi-Convolution Feature Extraction and Recurrent Neural Network Dependent Model for Short-Term Load Forecasting

Electric consumption prediction methods are investigated for many reasons, such as decision-making related to energy efficiency as well as for anticipating demand and the dynamics of the energy market. The objective of the present work is to compare two Deep Learning models, namely the Long Short-Term Memory (LSTM) model, and the Bi-directional LSTM (BLSTM) for univariate electric consumption Time Series (TS) short-term forecast model. The Data Sets (DSs) were selected for their different contexts and scales, with the goal of assessing the robustness of the models. Four DSs were used, related to the power consumption of: (a) a household in France; (b) a university building in Santarém, Brazil; (c) the Tétouan city zones, in Morocco; and (d) the aggregated electric demand of Singapore. The metrics RMSE, MAE, MAPE and R2 were calculated in a TS cross-validation scheme. Friedman's test was applied to normalized RMSE (NRMSE) results, showing that BLSTM outperforms LSTM with statistically significant difference (p = 0.0455), corroborating the fact that bidirectional weight updating significantly improves the LSTM performance with respect to different scales of electric power consumption. The present work provides statistical evidence supporting the conclusion that BLSTM outperforms LSTM models according to the tests performed, based on a complete methodology for TS prediction, and also establishes a baseline for future investigation of electric consumption TS prediction. 

Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction

Short-term power consumption forecasting plays a critical role in the process of building the smart grid. However, it is very challenging as the power consumption series has strong randomness and volatility. In this paper, the authors proposed a novel domain fusion deep model based on convolutional neural network (CNN), long short-term memory (LSTM), and discrete wavelet transform (DWT) to deal with this task accurately. The proposed deep model has two channels: raw power consumption and DWT. The raw power consumption channel corresponding to time-domain feature extraction while the DWT channel is frequency domain. They extract time-domain and frequency-domain features individually by using CNN. CNN extracted time-domain, and frequency-domain features are merged as time-frequency fusion features, which fully reflect the changing power consumption trend. The time-frequency fusion features are fed into LSTM to mining the features which have a long-time dependency. The comprehensive features are the fusion of time-domain and frequency-domain features with a long-time dependency, which are utilized for power consumption forecasting. The proposed method is evaluated on two public nature data sets related to power consumption with multiple metrics. The comparative experimental analysis has confirmed the state-of-the-art performance of the proposed method for short-term power consumption forecasting.

/pdf/domain-fusion-cnn-lstm-for-short-term-power-consumption-550ho97e45.pdf

Domain Fusion CNN-LSTM for Short-Term Power Consumption Forecasting

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Domain Fusion CNN-LSTM for Short-Term Power Consumption Forecasting