Increasing global building energy demand, with the related economic and environmental impact, upsurges the need for the design of reliable energy demand forecast models. This work presents k CNN-LSTM, a deep learning framework that operates on the energy consumption data recorded at predefined intervals to provide accurate building energy consumption forecasts. k CNN-LSTM employs (i) k − means clustering – to perform cluster analysis to understand the energy consumption pattern/trend; (ii) Convolutional Neural Networks (CNN) – to extract complex features with non-linear interactions that affect energy consumption; and (iii) Long Short Term Memory (LSTM) neural networks – to handle long-term dependencies through modeling temporal information in the time series data. The efficiency and applicability of k CNN-LSTM were demonstrated using a real time building energy consumption data acquired from a four-storeyed building in IIT-Bombay, India. The performance of k CNN-LSTM was compared with the k -means variant of the state-of-the-art energy demand forecast models in terms of well-known quality metrics. It is also observed that the accurate energy demand forecast provided by k CNN-LSTM due to its ability to learn the spatio-temporal dependencies in the energy consumption data makes it a suitable deep learning model for energy consumption forecast problems.

A deep learning framework for building energy consumption forecast

Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks

The role of electric vehicles (EVs) in energy systems will be crucial over the upcoming years due to their environmental-friendly nature and ability to mitigate/absorb excess power from renewable energy sources. Currently, a significant focus is given to EV smart charging (EVSC) solutions by researchers and industries around the globe to suitably meet the EVs' charging demand while overcoming their negative impacts on the power grid. Therefore, effective EVSC strategies and technologies are required to address such challenges. This review paper outlines the benefits and challenges of the EVSC procedure from different points of view. The role of EV aggregator in EVSC, charging methods and objectives, and required infrastructure for implementing EVSC are discussed. The study also deals with ancillary services provided by EVSC and EVs' load forecasting approaches. Moreover, the EVSC integrated energy systems, including homes, buildings, integrated energy systems, etc., are reviewed, followed by the smart green charging solutions to enhance the environmental benefit of EVs. The literature review shows the efficiency of EVSC in reducing charging costs by 30 %, grid operational costs by 10 %, and renewable curtailment by 40 %. The study gives key findings and recommendations which can be helpful for researchers and policymakers. 

A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges

This study compared the methods used to forecast increases in power consumption caused by the rising popularity of electric vehicles (EVs). An excellent model for each region was proposed using multiple scaled geographical datasets over two years. EV charging volumes are influenced by various factors, including the condition of a vehicle, the battery’s state-of-charge (SOC), and the distance to the destination. However, power suppliers cannot easily access this information due to privacy issues. Despite a lack of individual information, this study compared various modeling techniques, including trigonometric exponential smoothing state space (i.e., Trigonometric, Box–Cox, Auto-Regressive-Moving-Average (ARMA), Trend, and Seasonality (TBATS)), autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), and long short-term memory (LSTM) modeling, based on past values and exogenous variables. The effect of exogenous variables was evaluated in macro- and micro-scale geographical areas, and the importance of historic data was verified. The basic statistics regarding the number of charging stations and the volume of charging in each region are expected to aid the formulation of a method that can be used by power suppliers.

https://www.mdpi.com/1996-1073/14/5/1487/pdf

Forecasting Charging Demand of Electric Vehicles Using Time-Series Models

https://www.econstor.eu/bitstream/10419/244063/1/169415131X.pdf

Predictive model of energy consumption for office building by using improved GWO-BP

https://www.econstor.eu/bitstream/10419/243668/1/1683607791.pdf

Short term electricity load forecasting for institutional buildings

This study forecasts electricity demand in a smart grid environment. We present a prediction method that uses a combination of forecasting values based on time-series clustering. The clustering of normalized periodogram-based distances and autocorrelation-based distances are proposed as the time-series clustering methods. Trigonometrical transformation, Box–Cox transformation, autoregressive moving average (ARMA) errors, trend and seasonal components (TBATS), double seasonal Holt–Winters (DSHW), fractional autoregressive integrated moving average (FARIMA), ARIMA with regression (Reg-ARIMA), and neural network nonlinear autoregressive (NN-AR) are used for demand forecasting based on clustering. The results show that the time-series clustering method performs better than the method using the total amount of electricity demand in terms of the mean absolute percentage error (MAPE).

https://www.mdpi.com/1996-1073/13/9/2377/pdf

Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid

This study was conducted to investigate the applicability of measuring internet traffic as an input of short-term electricity demand forecasts. We believe our study makes a significant contribution to the literature, especially in short-term load prediction techniques, as we found that Internet traffic can be a useful variable in certain models and can increase prediction accuracy when compared to models in which it is not a variable. In addition, we found that the prediction error could be further reduced by applying a new multivariate model called VARX, which added exogenous variables to the univariate model called VAR. The VAR model showed excellent forecasting performance in the univariate model, rather than using the artificial neural network model, which had high prediction accuracy in the previous study.

Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models

ABSTRACT This paper examines the dependence structure of industrial electricity demand and financial indicators (the Korea Composite Stock Price Index [KOSPI], Korean Securities Dealers Automated Quotations [KOSDAQ], exchange rate, government bonds and exports) using the copula dynamic conditional correlation with symmetric and asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models to forecast volatility. We investigated symmetric and asymmetric GARCH types, such as the standard, exponential, Glosten–Jagannathan–Runkle and asymmetric power models to fit the marginal distribution. The two types of elliptical copula, Gaussian and Student’s t-distributions, were also considered to investigate the tail dependence between financial and electricity time series. We analysed the monthly log returns for January 2002 to April 2020. The empirical results reveal that the best-fit models for the Akaike information criteria are the asymmetric GARCH models, specifically the exponential-GARCH (E-GARCH). Moreover, the asymmetric GARCH model is superior to the symmetric GARCH in terms of forecast volatility. Extreme tail dependence exists for the KOSDAQ and exports indicators with the electricity demand. The KOSPI, Korea’s primary stock market, is the best-fit financial variable and presents the highest forecasting accuracy.

Linear time-varying regression with copula–DCC–asymmetric–GARCH models for volatility: the co-movement between industrial electricity demand and financial factors

Yunsun Kim

Papers

Short term electricity load forecasting for institutional buildings

Forecasting Charging Demand of Electric Vehicles Using Time-Series Models

Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid

Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models

Linear time-varying regression with copula–DCC–asymmetric–GARCH models for volatility: the co-movement between industrial electricity demand and financial factors