Abstract
The use of electrical energy is directly proportional to the increase in global population, both concerning growing industrialization and rising residential demand. The need to achieve a balance between electrical energy production and consumption inspires researchers to develop forecasting models for optimal and economical energy use. Mostly, the residential and industrial sectors use metering sensors that only measure the consumed energy but are unable to manage electricity. In this paper, we present a comparative analysis of a variety of deep features with several sequential learning models to select the optimized hybrid architecture for energy consumption prediction. The best results are achieved using convolutional long short-term memory (ConvLSTM) integrated with bidirectional long short-term memory (BiLSTM). The ConvLSTM initially extracts features from the input data to produce encoded sequences that are decoded by BiLSTM and then proceeds with a final dense layer for energy consumption prediction. The overall framework consists of preprocessing raw data, extracting features, training the sequential model, and then evaluating it. The proposed energy consumption prediction model outperforms existing models over publicly available datasets, including Household and Korean commercial building datasets.
Highlights
The precise prediction of energy consumption in residential and industrial sectors assists smart homes and grids to manage the demand of occupants efficiently and establish policies for energy preservation
Four common evaluation metrics are used to evaluate the proposed models and comparative analysis. These four evaluation matrices are mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), which are mathematically expressed in Equations (11)–(14), respectively
We provided a comparative analysis of various sequential learning models and selected the optimum one as the proposed model after extensive experimental findings
Summary
The precise prediction of energy consumption in residential and industrial sectors assists smart homes and grids to manage the demand of occupants efficiently and establish policies for energy preservation. The smart grid is the main hub acting as a supervisor to keep the balance or act as a bridge between production and consumption through using appropriate scheduling and management policies to avoid wasteful energy generation and financial loss [4]. For this purpose, energy forecasting methods play a key role in maintaining stability and ensuring proper planning between producers and consumers [5]. Numerous prediction models have been proposed that are mainly focused on reducing the prediction error rate and improving the quality of the power grids by optimizing energy use
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