Electricity Demand Model Research Articles

Modelling and forecasting hourly electricity demand in West African countries

The Economic Community of West African States aims to achieve 100% electrification rates by 2030 in all member countries. To achieve this ambitious target, electricity generation capacities need to be increased significantly. Forecasting hourly electricity demand is imperative for capacity planners in optimizing investment options and ensuring reliable electricity supply. However, modelling hourly electricity demand in developing countries can be a challenge due to paucity of historical demand data and methodological frameworks that adequately capture technology transitions and urban-rural communities. In this study, we address this gap by developing an hourly electricity demand model for 14 West African countries in the year 2016 and 2030. The model takes into account electrification rates, available household appliances, occupancy patterns of household members, type of day, available daylight hours and hourly weather conditions. Annual electricity demand in non-residential sectors and electricity access rates in urban and rural households are forecasted using multiple regression analysis. We validated the developed model using actual 2016 monthly and annual electricity demand data. The results show the seasonal variations of electricity demand, with hourly electricity demand in dry seasons relatively higher than demand in wet seasons. The results also indicate that in 2030, electricity demand in the West African region is estimated to be five times its 2016 level. The methodology presented in this study can be applicable for modelling hourly electricity demand in developing countries that have scarce historical hourly demand data, a significant electricity supply-demand gap, and varying electricity access rates in urban and rural areas.

Smart Grid Technologies in Gulf Cooperation Council (GCC) Countries: Challenges and Opportunities

There are structural shift in electricity markets in the Gulf Cooperation Council (GCC) countries, in both supply and demand industries. Therefore, the GCC member states need to develop new models for electricity supply and demand in order to adapt to these changes. Although, it requires huge capital investment and significant reforms to achieve this goal, however, in the long run, it is worth it. The countries in the region may need to privatize their government power industries, encourage competitive markets instead of monopolies, diversify electricity generation from fossil fuel based system by integrating renewable energy sources in mix generation and transform regulated electricity prices to liberalized electricity prices. The penetration of renewable energy into existing grid networks in the GCC region, is an economical way to diversify the electricity mix in the region and create an avenue for the implementation of smart grid technologies. Solar and wind power are the most common renewable sources in the region. The smart grid technology enables multi-directional flows, control of electricity and information in a wide distributed network, hence, could be referred to as the next generation of power grids. The traditional power grids could be made smart by making them communicate, sense, control and applying intelligence. Therefore, the smart grid technologies give room for effective compatibility and optimization of variables of the power grid for optimal generation, transmission and distribution system. This paper presents some of the challenges and opportunities in the implementation of smart grid technologies in the GCC region. The opportunities of smart grid implementation in the GCC region outweigh the challenges that could arise. The paper also addresses electricity consumption, interconnection grid of the GCC members, concepts of traditional and smart grid network, including renewable energy penetration in the GCC region. Â Â

Robust Day-Ahead Forecasting of Household Electricity Demand and Operational Challenges

In the recent years, several short-term forecasting models of household electricity demand have been proposed in the literature. This is partly due to emerging smart-grid applications, which require these kinds of forecasts to manage systems such as smart homes, prosumer aggregations, etc., and partly thanks to the availability of data from smart meters, which enable the development of such models. Since most models are academically developed, they often do not address challenges related to their implementation in a real-world environment. In the latter case, several issues arise, related to data quality and availability, which affect the operational performance and robustness of a forecasting system. In this paper, we design a hierarchical forecasting framework based on a total of 5 probabilistic models of varying complexity, after analyzing the respective performance and advantages of the models with an offline dataset. This multi-layered framework is necessary to address the various problematic situations occurring in practice and abide by the requirements for a real-world deployment. The forecasting system is deployed in a real-world case and evaluated here on data from 20 households. Field data, comprising forecasts and measurements, are analyzed for each household. A detailed comparison is drawn between the online and offline performances. Since a notable degradation is observed in the operational environment, we discuss at length the reasons for such an effect. We determine that the exact settings of the training and test periods are marginally responsible, but that the main cause is the intrinsic evolution of the demand time series, which hinders the forecasting performance. This evolution is due to unknown household characteristics that need to be monitored to provide more adaptable models.

Improvement of performance of short term electricity demand model with meteorological parameters

The accuracy of short term electricity demand forecasting is essential for operation and trading activities on energy market. This paper considers a parsimonious forecasting model to explain the importance of sophisticated weather parameters for hourly electricity demand forecasting. Temperature is the major factor that directly influence electricity demand, but what about the affect of other weather factors such as relative humidity, wind speed etc. on short term electricity demand forecasting, is the prime research question and this paper analyzed it quantitatively. We demonstrate three different multiple linear models including auto-regressive moving average ARMA (2,6) models with and without some exogenous weather variables to compare with performance for Hokkaido Prefecture, Japan. Since, Bayesian approach is used to estimate the weight of each variables with Gibbs sampling, it generates the weight of coefficients in terms of distribution as our interest. The performance of each models for complete one year out sample prediction shows that the average improvement of hourly forecast by 1 to 2 % can be achieve by including such weather factors.

Vision 2023: Scrutinizing achievability of Turkey's electricity capacity targets and generating scenario based nationwide electricity demand forecasts

In the last couple of years, Turkey faced serious economic and geopolitical problems. As a result, many from the academia and industry now have suspicions about the viability of Turkey's Vision 2023 electricity targets. Consequently, in this study an up to date forecast of Turkey's electricity demand till 2023 is generated and achievability of the Vision 2023 electricity capacity targets are scrutinized. Turkey's electricity demand till 2023 is forecasted using the semi-empirical electricity demand model generated in this study. Different average % annual electricity demand per capita growth scenarios are used for the electricity demand modelling. Results from the modelling study showed that Turkey's electricity demand in 2023 could be 327,000 GWh if the very low demand scenario occurs or 362,000 GWh if the low demand growth scenario prevails or 386,000 GWh if the medium demand growth scenario succeeds or 429,000 GWh if the high demand growth scenario happens. The demand forecasts generated in this study match 94.3% (or higher) with previous forecasts found from the literature. The scrutiny showed that wind energy installed capacity in 2023 (estimated at nearly 11,000 MW) might be significantly lower than the official target of 20,000 MW. It is also pinpointed that one nuclear power plant might become operational (25% operational – one out of four units/reactors) instead of the planned two in 2023. Putting these potential target deviations aside, the scrutiny showed that Turkey's installed capacity in 2023 would most probably be greater than 110,000 MW and might in fact reach to the planned 120,000 MW. This is an important finding for policy analysts and researchers. Finally, it is suggested that the work on Turkey's energy projects must be accelerated and some of the Vision 2023 installed capacity targets (wind and nuclear) should be revised by the authorities.

Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect.

After the severe nuclear disaster in Fukushima, which was triggered by the Great East Japan earthquake in March 2011, nuclear power plants in Japan were temporarily shut down for mandatory inspections. To prevent large-scale blackouts, the Japanese government requested companies and households to reduce electricity consumption in summer and winter. It is reported that the domestic electricity demand had a structural decrease because of the electricity conservation effect (ECE). However, quantitative analysis of the ECE is not sufficient, and especially time variation of the ECE remains unclear. Understanding the ECE is important because Japan’s NDC (nationally determined contribution) assumes the reduction of CO2 emissions through aggressive energy conservation. In this study, we develop a time series model of monthly electricity demand in Japan and estimate time variation of the ECE. Moreover, we evaluate the impact of electricity conservation on CO2 emissions from power plants. The dynamic linear model is used to separate the ECE from the effects of other irrelevant factors (e.g. air temperature, economic production, and electricity price). Our result clearly shows that consumers’ electricity conservation behavior after the earthquake was not temporary but became established as a habit. Between March 2011 and March 2016, the ECE on industrial electricity demand ranged from 3.9% to 5.4%, and the ECE on residential electricity demand ranged from 1.6% to 7.6%. The ECE on the total electricity demand was estimated at 3.2%–6.0%. We found a seasonal pattern that the residential ECE in summer is higher than that in winter. The emissions increase from the shutdown of nuclear power plants was mitigated by electricity conservation. The emissions reduction effect was estimated at 0.82 MtCO2–2.26 MtCO2 (−4.5% on average compared to the zero-ECE case). The time-varying ECE is necessary for predicting Japan’s electricity demand and CO2 emissions after the earthquake.

Habits and rational behaviour in residential electricity demand

Households make an investment analysis when buying new electrical appliances. Therefore, expectations about future electricity consumption may have an impact on current consumption and investment decisions. Dynamic partial adjustment models of residential electricity demand neglect rational consumer behaviour. In this paper we propose a model for residential electricity demand that allows for forward-looking behaviour. We estimate lead consumption models using two stages least squares fixed effects on a panel of 48 US states between 1995 and 2011. We find that expectations about future consumption have an impact on current consumption decisions. This novel approach may improve our understanding of the dynamics of residential electricity demand and the evaluation of the effects of energy policies.

A vector autoregression weather model for electricity supply and demand modeling

Weather forecasting is crucial to both the demand and supply sides of electricity systems. Temperature has a great effect on the demand side. Moreover, solar and wind are very promising renewable energy sources and are, thus, important on the supply side. In this paper, a large vector autoregression (VAR) model is built to forecast three important weather variables for 61 cities around the United States. The three variables at all locations are modeled as response variables. Lag terms are used to capture the relationship between observations in adjacent periods and daily and annual seasonality are modeled to consider the correlation between the same periods in adjacent days and years. We estimate the VAR model with 16 years of hourly historical data and use two additional years of data for out-of-sample validation. Forecasts of up to six-hours-ahead are generated with good forecasting performance based on mean absolute error, root mean square error, relative root mean square error, and skill scores. Our VAR model gives forecasts with skill scores that are more than double the skill scores of other forecasting models in the literature. Our model also provides forecasts that outperform persistence forecasts by between 6% and 80% in terms of mean absolute error. Our results show that the proposed time series approach is appropriate for very short-term forecasting of hourly solar radiation, temperature, and wind speed.

Modelling of electricity demand in residential buildings using artificial neural networks

Electricity is the basis for the functioning of modern society. It is used for many purposes, including HVAC systems. Information on future electricity demand is an important element from the point of view of both the real estate user and other entities on the energy market. The study forecasts the demand for electricity on the basis of data from over 12,000 buildings. The model was created using one of the tools from the area of artificial intelligence - neural networks. Over 15,000 networks differing in architecture, number of nerve cells, activation functions, sets of explanatory variables and learning algorithms have been tested. The paper presents those from the tested models, which were characterized by the highest precision of operation.

On the Use of Functional Additive Models for Electricity Demand and Price Prediction

This paper presents an application of functional additive models in the context of electricity demand and price prediction. Data from the Spanish electricity market are used to obtain the pointwise predictions. Also prediction intervals, based on a bootstrap procedure, are computed. This approach is compared with the use of other functional regression methods applied to the same data set by Aneiros et al. (2016).

A stochastic modelling and simulation approach to heating and cooling electricity consumption in the residential sector

Heating and cooling consumption is one of the most significant terms in the total supply, which may come to represent half of the total demand in European countries. These appliances are supplied by a wide range of sources, being electrical devices of special interest in the Smart Grid. Current tools allow the assessment of the consumption with a high accuracy, nevertheless, they lack the temporal resolution or low-level details to study advanced control techniques. In this context, bottom-up stochastic models are a main tool to simulate high-resolution demand profiles. This paper presents a 1-min resolution model for electricity demand of heating and cooling appliances. The system is based on the simulation of individual households considering variables such as the number of residents, location, type of day (weekday or weekend) and date. It was used to simulate daily profiles which showed two main demand peaks, one during mornings and another during dinner time, for heating, and a high demand during midday for cooling consumption. Moreover, annual simulations depicted the importance of cooling appliances, which despite having a lower annual demand, can overcharge the grid with their concurrent utilisation, highlighting the usefulness of this tool for studying the impact of these devices.

Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia

Accurate and reliable forecasting models for electricity demand (G) are critical in engineering applications. They assist renewable and conventional energy engineers, electricity providers, end-users, and government entities in addressing energy sustainability challenges for the National Electricity Market (NEM) in Australia, including the expansion of distribution networks, energy pricing, and policy development. In this study, data-driven techniques for forecasting short-term (24-h) G-data are adopted using 0.5 h, 1.0 h, and 24 h forecasting horizons. These techniques are based on the Multivariate Adaptive Regression Spline (MARS), Support Vector Regression (SVR), and Autoregressive Integrated Moving Average (ARIMA) models. This study is focused in Queensland, Australia’s second largest state, where end-user demand for energy continues to increase. To determine the MARS and SVR model inputs, the partial autocorrelation function is applied to historical (area aggregated) G data in the training period to discriminate the significant (lagged) inputs. On the other hand, single input G data is used to develop the univariate ARIMA model. The predictors are based on statistically significant lagged inputs and partitioned into training (80%) and testing (20%) subsets to construct the forecasting models. The accuracy of the G forecasts, with respect to the measured G data, is assessed using statistical metrics such as the Pearson Product-Moment Correlation coefficient (r), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Normalized model assessment metrics based on RMSE and MAE relative to observed means (RMSEG¯andMAEG¯), Willmott’s Index (WI), Legates and McCabe Index (ELM), and Nash–Sutcliffe coefficients (ENS) are also utilised to assess the models’ preciseness. For the 0.5 h and 1.0 h short-term forecasting horizons, the MARS model outperforms the SVR and ARIMA models displaying the largest WI (0.993 and 0.990) and lowest MAE (45.363 and 86.502 MW), respectively. In contrast, the SVR model is superior to the MARS and ARIMA models for the daily (24 h) forecasting horizon demonstrating a greater WI (0.890) and MAE (162.363 MW). Therefore, the MARS and SVR models can be considered more suitable for short-term G forecasting in Queensland, Australia, when compared to the ARIMA model. Accordingly, they are useful scientific tools for further exploration of real-time electricity demand data forecasting.

Modeling of Electricity Demand for Azerbaijan: Time-Varying Coefficient Cointegration Approach

Recent literature has shown that electricity demand elasticities may not be constant over time and this has investigated using time-varying estimation methods. As accurate modeling of electricity demand is very important in Azerbaijan, which is a transitional country facing significant change in its economic outlook, we analyze whether the response of electricity demand to income and price is varying over time in this economy. We employed the Time-Varying Coefficient cointegration approach, a cutting-edge time-varying estimation method. We find evidence that income elasticity demonstrates sizeable variation for the period of investigation ranging from 0.48% to 0.56%. The study has some useful policy implications related to the income and price aspects of the electricity consumption in Azerbaijan.

The long-term scenario and greenhouse gas effects cost-benefit analysis of Iran's electricity sector

The electricity sector has a key role in Iran's socio-economic development. Its contribution to carbon dioxide emissions is 30.2%. Therefore, it is the biggest environmental pollution source in Iran. In this study, the current circumstance of electricity supply and demand in Iran is investigated to find the possible trends for future power generation to predict and answer the questions about the amount of future electricity demand. Electricity demand is based on economics and environmental conditions. In this regard, econometrics method is applied to obtain the electricity demand model in three scenarios until 2040. LEAP model would simulate the supply side for the next three decades in business as usual, low-carbon and renewable energy scenarios based on various possible policies. The results show that the share of fossil fuels from total electricity production remains over 90% and the carbon dioxide emission reaches 668.2 million tonnes in the BAU scenario in 2040. If the policies for the development of renewable technologies are adopted, the share of fossil resources, carbon dioxide emissions decrease to 33% and 294.6 million tonnes respectively.

The Implications of Demand Response Measures and Electrification of Transport on UK Household Energy Demand and Consumption

This study has been undertaken to gain a better understanding on how the residential electricity demand and consumption values might evolve in the medium term in a future built environment benefiting from renewable energy systems and storage technologies. Analysis and modeling of winter and summer electricity demand and consumption data in four scenarios for 2030 was performed, after the establishment of a baseline scenario in 2015 (BS 2015). The scenarios in 2030 included the business as usual scenario (BAU 2030), a scenario assuming electrification of heating and energy efficiency measures (EE 2030), a scenario in which demand response measures are also considered (DR 2030) and a scenario in which one electric vehicle (EV) is assumed for each house as well (Te 2030). Electricity demand and consumption ranges for different scales at the distribution level for each scenario were derived. It was concluded that properties with currently low peak demand values are bound to experience a much higher peak in the early morning hours in winter under the Te 2030 scenario than properties with already high peak demand. This would signify a new peak at a new time. In terms of electricity consumption in 2030, the energy efficiency measures would counterbalance the increase of electricity consumption due to the inclusion of the EV in winter, so the consumption in Te 2030 is found to be similar to the consumption in BAU 2030. The analysis also demonstrated the need to explore the potential role of thermal storage versus electricity storage in buildings.

Dynamic models of residential electricity demand: Evidence from Switzerland

We estimate the short- and long-run elasticities of electricity demand in Switzerland using a dynamic model of residential electricity consumption incorporating a correction introduced by Kiviet. We find that the short-run elasticity of residential demand for electricity in Switzerland is around −0.3 while the long-run elasticity is around −0.6. Our estimates indicate that pricing policy as a plan for energy strategy may have a moderate impact on residential customers in the short run but will have a stronger influence in the long-run. In view of the recent proposal in Switzerland to introduce a tax on electricity as part of its energy strategy plan, an increase in the price of electricity may result in a moderate decrease in electricity consumption.

A Smart Forecasting Approach to District Energy Management

This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs) (parallel ANNs) based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the ANN by considering external weather conditions, occupancy type, main income providers’ employment status and related variables for the fuel poverty index. Moreover, a detailed parameter tuning is conducted using various configurations for each individual ANN. The study also demonstrates the strength of the parallel ANN models in different seasons of the years. In the proposed district level energy forecasting model, the training and testing stages of parallel ANNs utilise dataset of a group of six buildings. The aim of each individual ANN is to predict electricity consumption and the aggregated demand in sub-hourly time-steps. The inputs of each ANN are determined using Principal Component Analysis (PCA) and Multiple Regression Analysis (MRA) methods. The accuracy and consistency of ANN predictions are evaluated using Pearson coefficient and average percentage error, and against four seasons: winter, spring, summer, and autumn. The lowest prediction error for the aggregated demand is about 4.51% for winter season and the largest prediction error is found as 8.82% for spring season. The results demonstrate that peak demand can be predicted successfully, and utilised to forecast and provide demand-side flexibility to the aggregators for effective management of district energy systems.

Energy Consumption and GGP in the OECD Countries: A Causality Analysis

Abstract This study investigated the nature or direction of causality between GDP, electricity consumption and total energy consumption in the OECD. Secondary data was used while both the ordinary least square (OLS) and generalized method of moments (GMM) estimators were employed to test for causality in our model. Our result found the presence of a bi-directional causality between energy consumption and GDP for the total energy demand model and between electricity consumption and GDP for the electricity demand model. By implication, the bi-directional causality in our estimated models suggest that both energy consumption and GDP are important factors in economic development in the OECD. Thus, if misguided policy measures are made to reduce energy consumption it could have a detrimental effect on GDP which will slow down economic growth. A recommendation is for policy makers to concentrate on encouraging energy efficiency as a way to reduce energy and electricity consumption.

Modelling non-stationary time series using a peaks over threshold distribution with time varying covariates and threshold: An application to peak electricity demand

Long term peak electricity demand forecasting is a crucial step in the process of planning for power transmission and new generation capacity. This paper discusses an application of the Generalized Pareto Distribution to the modelling of daily peak electricity demand using South African data for the period 2000 to 2010. The main contribution of this paper is in the use of a cubic smoothing spline with a constant shift factor as a time varying threshold. An intervals estimator method is then used to decluster the observations above the threshold. We explore the influence of temperature by including it as a covariate in the Generalized Pareto Distribution parameters. A comparative analysis is done using the block maxima approach. The GPD model showed a better fit to the data compared to the GEVD model. Key findings from this study are that the Weibull class of distributions best fits the data which is bounded from above for both stationary and non-stationary models. Another key finding is that for different values of the temperature covariate the shape parameter is invariant and the scale parameter changes for different values of heating degree days.

Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques

Policymakers in developing and transitional economies require sound models to: (i) understand the drivers of rapidly growing energy consumption and (ii) produce forecasts of future energy demand. This paper attempts to model electricity demand in Azerbaijan and provide future forecast scenarios—as far as we are aware this is the first such attempt for Azerbaijan using a comprehensive modelling framework. Electricity consumption increased and decreased considerably in Azerbaijan from 1995 to 2013 (the period used for the empirical analysis)—it increased on average by about 4% per annum from 1995 to 2006 but decreased by about 4½% per annum from 2006 to 2010 and increased thereafter. It is therefore vital that Azerbaijani planners and policymakers understand what drives electricity demand and be able to forecast how it will grow in order to plan for future power production. However, modeling electricity demand for such a country has many challenges. Azerbaijan is rich in energy resources, consequently GDP is heavily influenced by oil prices; hence, real non-oil GDP is employed as the activity driver in this research (unlike almost all previous aggregate energy demand studies). Moreover, electricity prices are administered rather than market driven. Therefore, different cointegration and error correction techniques are employed to estimate a number of per capita electricity demand models for Azerbaijan, which are used to produce forecast scenarios for up to 2025. The resulting estimated models (in terms of coefficients, etc.) and forecasts of electricity demand for Azerbaijan in 2025 prove to be very similar; with the Business as Usual forecast ranging from about of 19½ to 21 TWh.