A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons

With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly

LOAD FORECASTING FOR DAILY LOAD OPERATIONAL PLAN USING LSTM (CASE STUDY: SOUTH SULAWESI SUB SYSTEM)

The electrical load required in an electricity sub-system changes every day. Electric power operators must be able to generate and distribute electricity according to consumer needs. In the Sulawesi sub-system, the power plants used are still dominated by fossil fuel generators, so that in their operations, fuel requirements need to be given serious attention. Planning a good daily electricity consumption is needed so that the fuel cost becomes optimal. In the current condition, the load forecasting for the Daily Load Operation Plan (ROH) is still based on Expert Judgment, which is different for each forecaster. With a fairly large error tolerance limit of 4%. We need a load forecasting instrument capable of better error tolerance. Forecasting methods such as ARIMA, SARIMA and ARIMAX have been used for many years. In recent years, several artificial intelligence techniques such as Neural Network and machine learning have been developed for time series analysis. And recently, more accurate forecasting results are shown by Artificial Neural Network (ANN) and Recurrent Neural Network (RNN) compared to traditional forecasting methods. Long Short Term Memory (LSTM) is a model of RNN that uses past data (Long Term) to predict current data (Short Term). Electric load in Sulawesi subsystem used as data training after normalized using min-max normalization. The LSTM model is made with different data input. Forecasting  performance of each model is then evaluated based on the RMSE and MAPE values. Of the several data input models, forecasting models with daily data input show better performance than other scenarios. The MAPE and RMSE values obtained were 2.384% and 33.95, respectively

Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

[EN] Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calendar effect. The multiple seasonal Holt-Winters models are widely used due to the great precision and simplicity that they offer. Usually, these models relate this calendar effect to external variables that contribute to modification of their forecasts a posteriori. In this work, a new point of view is presented, where the calendar effect constitutes a built-in part of the Holt-Winters model. In particular, the proposed model incorporates discrete-interval moving seasonalities. Moreover, a clear example of the application of this methodology to situations that are difficult to treat, such as the days of Easter, is presented. The results show that the proposed model performs well, outperforming the regular Holt-Winters model and other methods such as artificial neural networks and Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS) methods.The authors would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-8888209C2-1-R.Trull, Ó.; García-Díaz, JC.; Troncoso, A. (2019). Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies. 12(6):1-16. https://doi.org/10.3390/en12061083S116126Garrués-Irurzun, J., & López-García, S. (2009). Red Eléctrica de España S.A.: Instrument of regulation and liberalization of the Spanish electricity market (1944–2004). Renewable and Sustainable Energy Reviews, 13(8), 2061-2069. doi:10.1016/j.rser.2009.01.028Roldan-Fernandez, J., Gómez-Quiles, C., Merre, A., Burgos-Payán, M., & Riquelme-Santos, J. (2018). Cross-Border Energy Exchange and Renewable Premiums: The Case of the Iberian System. Energies, 11(12), 3277. doi:10.3390/en11123277Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. IEEE Transactions on Power Systems, 18(3), 1014-1020. doi:10.1109/tpwrs.2002.804943Juberias, G., Yunta, R., Garcia Moreno, J., & Mendivil, C. (1999). A new ARIMA model for hourly load forecasting. 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333). doi:10.1109/tdc.1999.755371Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421. doi:10.1016/j.energy.2009.06.034Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8), 799-805. doi:10.1057/palgrave.jors.2601589Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204(1), 139-152. doi:10.1016/j.ejor.2009.10.003Ko, C.-N., & Lee, C.-M. (2013). Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy, 49, 413-422. doi:10.1016/j.energy.2012.11.015Rana, M., & Koprinska, I. (2016). Forecasting electricity load with advanced wavelet neural networks. Neurocomputing, 182, 118-132. doi:10.1016/j.neucom.2015.12.004Baliyan, A., Gaurav, K., & Mishra, S. K. (2015). A Review of Short Term Load Forecasting using Artificial Neural Network Models. Procedia Computer Science, 48, 121-125. doi:10.1016/j.procs.2015.04.160Yang, Z., Ce, L., & Lian, L. (2017). Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Applied Energy, 190, 291-305. doi:10.1016/j.apenergy.2016.12.130Ghadimi, N., Akbarimajd, A., Shayeghi, H., & Abedinia, O. (2018). Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy, 161, 130-142. doi:10.1016/j.energy.2018.07.088Troncoso Lora, A., Riquelme Santos, J. M., Riquelme, J. C., Gómez Expósito, A., & Martínez Ramos, J. L. (2004). Time-Series Prediction: Application to the Short-Term Electric Energy Demand. Lecture Notes in Computer Science, 577-586. doi:10.1007/978-3-540-25945-9_57Martinez Alvarez, F., Troncoso, A., Riquelme, J. C., & Aguilar Ruiz, J. S. (2011). Energy Time Series Forecasting Based on Pattern Sequence Similarity. IEEE Transactions on Knowledge and Data Engineering, 23(8), 1230-1243. doi:10.1109/tkde.2010.227Cancelo, J. R., Espasa, A., & Grafe, R. (2008). Forecasting the electricity load from one day to one week ahead for the Spanish system operator. International Journal of Forecasting, 24(4), 588-602. doi:10.1016/j.ijforecast.2008.07.005TORRÓ, H., MENEU, V., & VALOR, E. (2003). Single Factor Stochastic Models with Seasonality Applied to Underlying Weather Derivatives Variables. The Journal of Risk Finance, 4(4), 6-17. doi:10.1108/eb022969Darbellay, G. A., & Slama, M. (2000). Forecasting the short-term demand for electricity. International Journal of Forecasting, 16(1), 71-83. doi:10.1016/s0169-2070(99)00045-xMoral-Carcedo, J., & Vicéns-Otero, J. (2005). Modelling the non-linear response of Spanish electricity demand to temperature variations. Energy Economics, 27(3), 477-494. doi:10.1016/j.eneco.2005.01.003Erişen, E., Iyigun, C., & Tanrısever, F. (2017). Short-term electricity load forecasting with special days: an analysis on parametric and non-parametric methods. Annals of Operations Research. doi:10.1007/s10479-017-2726-6Arora, S., & Taylor, J. W. (2013). Short-Term Forecasting of Anomalous Load Using Rule-Based Triple Seasonal Methods. IEEE Transactions on Power Systems, 28(3), 3235-3242. doi:10.1109/tpwrs.2013.2252929Arora, S., & Taylor, J. W. (2018). Rule-based autoregressive moving average models for forecasting load on special days: A case study for France. European Journal of Operational Research, 266(1), 259-268. doi:10.1016/j.ejor.2017.08.056Bermúdez, J. D. (2013). Exponential smoothing with covariates applied to electricity demand forecast. European J. of Industrial Engineering, 7(3), 333. doi:10.1504/ejie.2013.054134Göb, R., Lurz, K., & Pievatolo, A. (2013). Electrical load forecasting by exponential smoothing with covariates. Applied Stochastic Models in Business and Industry, 29(6), 629-645. doi:10.1002/asmb.2008Chatfield, C. (1978). The Holt-Winters Forecasting Procedure. Applied Statistics, 27(3), 264. doi:10.2307/234716

Electrical load forecasting models: a critical systematic review

Electricity forecasting is an essential component of smart grid, which has attracted increasing academic interest. Forecasting enables informed and efficient responses for electricity demand. However, various forecasting models exist making it difficult for inexperienced researchers to make an informed model selection. This paper presents a systematic review of forecasting models with the main purpose of identifying which model is best suited for a particular case or scenario. Over 113 different case studies reported across 41 academic papers have been used for the comparison. The timeframe, inputs, outputs, scale, data sample size, error type and value have been taken into account as criteria for the comparison. The review reveals that despite the relative simplicity of all reviewed models, the regression and/or multiple regression are still widely used and efficient for long and very long-term prediction. For short and very short-term prediction, machine-learning algorithms such as artificial neural networks, support vector machines, and time series analysis (including Autoregressive Integrated Moving Average (ARIMA) and the Autoregressive Moving Average (ARMA)) are favoured. The most widely employed independent variables are the building and occupancy characteristics and environmental data, especially in the machine learning models. In many cases, time series analysis and regressions rely on electricity historical data only, without the introduction of exogenous variables. Overall, if the singularity of the different cases made the comparison difficult, some trends are clearly identifiable. Considering the large amount of use cases studied, the meta-analysis of the references led to the identification of best practices within the expert community in relation to forecasting use for electricity consumption and power load prediction. Therefore, from the findings of the meta-analysis, a taxonomy has been defined in order to help researchers make an informed decision and choose the right model for their problem (long or short term, low or high resolution, building to country level)

Short-term load forecasting based on a semi-parametric additive model

Short-term load forecasting is an essential instrument in power system planning, operation and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the contrary, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance. In this paper, semi-parametric additive models are proposed to estimate the relationships between demand and the driver variables. Specifically, the inputs for these models are calendar variables, lagged actual demand observations and historical and forecast temperature traces for one or more sites in the target power system. In addition to point forecasts, prediction intervals are also estimated using a modified bootstrap method suitable for the complex seasonality seen in electricity demand data. The proposed methodology has been used to forecast the half-hourly electricity demand for up to seven days ahead for power systems in the Australian National Electricity Market. The performance of the methodology is validated via out-of-sample experiments with real data from the power system, as well as through on-site implementation by the system operator.Short-term load forecasting, additive model, time series, forecast distribution

Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series

Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessary include the target series. In the present study, we investigate the performance of this special case of STLF, called transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement NN model and perform a clustering analysis to identify similar patterns among the series and assist TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered

Impact study of temperature on the time series electricity demand of urban Nepal for short-term load forecasting

Short-term electricity demand forecasting is one of the best ways to understand the changing characteristics of demand that helps to make important decisions regarding load flow analysis, preventing imbalance in generation planning, demand management, and load scheduling, all of which are actions for the reliability and quality of that power system. The variation in electricity demand depends upon various parameters, such as the effect of the temperature, social activities, holidays, the working environment, and so on. The selection of improper forecasting methods and data can lead to huge variations and mislead the power system operators. This paper presents a study of electricity demand and its relation to the previous day’s lags and temperature by examining the case of a consumer distribution center in urban Nepal. The effect of the temperature on load, load variation on weekends and weekdays, and the effect of load lags on the load demand are thoroughly discussed. Based on the analysis conducted on the data, short-term load forecasting is conducted for weekdays and weekends by using the previous day’s demand and temperature data for the whole year. Using the conventional time series model as a benchmark, an ANN model is developed to track the effect of the temperature and similar day patterns. The results show that the time series models with feedforward neural networks (FF-ANNs), in terms of the mean absolute percentage error (MAPE), performed better by 0.34% on a weekday and by 8.04% on a weekend

Weather corrected electricity demand forecasting

Electricity load forecasts now form an essential part of the routine operations of electricity companies. The complexity of the short-term load forecasting (STLF) problem arises from the multiple seasonal components, the change in consumer behaviour during holiday seasons and other social and religious events that affect electricity consumption. The aim of this research is to produce models for electricity demand that can be used to further the understanding of the dynamics of electricity consumption in South Wales. These models can also be used to produce weather corrected forecasts, and to provide short-term load forecasts. Two novel time series modelling approaches were introduced and developed. Profiles ARIMA (PARIMA) and the Variability Decomposition Method (VDM). PARIMA is a univariate modelling approach that is based on the hierarchical modelling of the different components of the electricity demand series as deterministic profiles, and modelling the remainder stochastic component as ARIMA, serving as a simple yet versatile signal extraction procedure and as a powerful prewhitening technique. The VDM is a robust transfer function modelling approach that is based on decomposing the variability in time series data to that of inherent and external. It focuses the transfer function model building on explaining the external variability of the data and produces models with parameters that are pertinent to the components of the series. Several candidate input variables for the VDM models for electricity demand were investigated, and a novel collective measure of temperature the Fair Temperature Value (FTV) was introduced. The FTV takes into account the changes in variance of the daily maximum and minimum temperatures with time, making it a more suitable explanatory variable for the VDM model. The novel PARIMA and VDM approaches were used to model the quarterly, monthly, weekly, and daily demand series. Both approaches succeeded where existing approaches were unsuccessful and, where comparisons are possible, produced models that were superior in performance. The VDM model with the FTV as its explanatory variable was the best performing model in the analysis and was used for weather correction. Here, weather corrected forecasts were produced using the weather sensitive components of the PARIMA models and the FTV transfer function component of the VDM model

Energy consumption forecasting using machine learning

Forecasting electricity demand and consumption accurately is critical to the optimal and costeffective operation system, providing a competitive advantage to companies. In working with seasonal data and external variables, the traditional time-series forecasting methods cannot be applied to electricity consumption data. In energy planning for a generating company, accurate power forecasting for the electrical consumption prediction, as a technique, to understand and predict the market electricity demand is of paramount importance. Their power production can be adjusted accordingly in a deregulated market. As data type is seasonal, Persistence Models (Naïve Models), Seasonal AutoRegressive Integrated Moving Averages with eXogenous regressors (SARIMAX), and Univariate Long-Short Term Memory Neural Network (LSTM) is used to explicitly deal with seasonality as a class of time-series forecasting models. The main purpose of this project is to perform exploratory data analysis of the Spain power, then use different forecasting models to once-daily predict the next 24 hours of energy demand and daily peak demand. To split the electricity consumption data from 2015 to 2018 into training and test sets, the first three years from 2015 and 2017 were used as the training set, while values from 2018 were used as the test set. The obtained results showed that the machine learning algorithms proposed in the recent literature outperformed the tested algorithms. Models are evaluated using root mean squared error (RMSE) to be directly comparable to energy readings in the data. RMSE has calculated two ways. First to represent the error of predicting each hour at a time (i.e. one error per-hourly slice). Second to represent the models’ overall performance. The results show that electricity demand can be modeled using machine learning algorithms, deploying renewable energy, planning for high/low load days, and reducing wastage from polluting on reserve standby generation, detecting abnormalities in consumption trends, and quantifying energy and cost-saving measures

Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion Laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework.Comment: Accepted by IEEE Transactions on Intelligent Vehicle