Long-term Demand Forecasting Research Articles

Multi-year load growth-based optimal planning of grid-connected microgrid considering long-term load demand forecasting: A case study of Tehran, Iran

Although much efforts have been devoted to the optimal design of the energy systems, there is a research gap about the multi-year load growth-based optimal planning of microgrids. This paper tries to fill such a research gap by developing a novel method for the optimal design of the grid-connected microgrids based on the long-term load demand forecasting. The multilayer perceptron artificial neural network is used for time-series load prediction. The impacts of the annual load growth are analyzed under various cases based on the consideration and determination methods of yearly load growth. The proposed method is applied to an actual microgrid in Tehran, Iran, using HOMER (Hybrid Optimization of Multiple Energy Resources) software. The load modeling’s capabilities of HOMER software, as a well-known software for the optimal design of energy systems, are used, which have received less attention. Since most existing research works in Iran focused on the off-grid operating mode, the study of an actual microgrid under grid-connected operating mode is one of the most contributions of this paper. The comparison of the obtained results and other available methods illustrate the impacts of the adequately precise estimation of annual load growth in the design of energy systems.

Analysis of Challenges Due to Changes in Net Load Curve in South Korea by Integrating DERs

The development of Distributed Energy Resources (DERs) is essential in accordance with the mandatory greenhouse gas (GHG) emission reduction policies, resulting in many DERs being integrated into the power system. Currently, South Korea is also focusing on increasing the penetration of renewable energy sources (RES) and EV by 2030 to reduce GHGs. However, indiscriminate DER development can give a negative impact on the operation of existing power systems. The existing power system operation is optimized for the hourly net load pattern, but the integration of DERs changes it. In addition, since ToU (Time-of-Use) tariff and Demand Response (DR) programs are very sensitive to changes in the net load curve, it is essential to predict the hourly net load pattern accurately for the modification of pricing and demand response programs in the future. However, a long-term demand forecast in South Korea provides only the total amount of annual load (TWh) and the expected peak load level (GW) in summer and winter seasons until 2030. In this study, we use the annual photovoltaic (PV) installed capacity, PV generation, and the number of EV based on the target values for 2030 in South Korea to predict the change in hourly net load curve by year and season. In addition, to predict the EV charging load curve based on Monte Carlo simulation, the EV users’ charging method, charging start time, and State-of-Charge (SoC) were considered. Finally, we analyze the change in hourly net load curve due to the integration of PV and EV to determine the amplification of the duck curve and peak load time by year and season, and present the risks caused by indiscriminate DERs development.

Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario

Purpose This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress. Design/methodology/approach A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models. Findings The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models. Originality/value The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.

Long-Term Electricity Supply and Demand Forecast (2018–2040): A LEAP Model Application towards a Sustainable Power Generation System in Ecuador

This research assesses the Ecuadorian power generation system, estimating the electricity supply and demand forecast until 2040. For this purpose, three potential alternative scenarios were analyzed using the Long-range Energy Alternatives Planning (LEAP) System; S1: Business As Usual; S2: Power Generation Master Plan; and S3: Sustainable Power Generation System. The main goal of this study is to analyze the possible alternatives for electricity supply and demand, fuel consumption, and the future structure of the Ecuadorian power generation system to transform the current system based on petroleum fuels into a sustainable system that consumes natural gas, and progressively introduces renewable power generation plants such as solar, wind, biomass, and hydroelectric until 2040. According to the estimated results through the inclusion of sustainable energy policies, S3 scenario relative to S1 scenario could reduce the average CO2 equivalent (CO2e) emissions by 11.72%, the average production costs by 9.78%, and the average petroleum fuel consumption by 15.95%. Consequently, a correct energy transition contributes to the protection of the environment and public health and has a direct effect on economic savings for the state, which benefits to improve the citizen’s quality of life.

Long-Term Demand Forecasting in a Scenario of Energy Transition

The energy transition from fossil fuels to carbon-free sources will be a big challenge in the coming decades. In this context, the long-term prediction of energy demand plays a key role in planning energy infrastructures and in adopting economic and energy policies. In this article, we aimed to forecast energy demand for Spain, mainly employing econometrics techniques. From information obtained from institutional databases, energy demand was decomposed into many factors and economy-related activity sectors, obtaining a set of disaggregated sequences of time-dependent values. Using time-series techniques, a long-term prediction was then obtained for each component. Finally, every element was aggregated to obtain the final long-term energy demand forecast. For the year 2030, an energy demand equivalent to 82 million tons of oil was forecast. Due to improvements in energy efficiency in the post-crisis period, a decoupling of economy and energy demand was obtained, with a 30% decrease in energy intensity for the period 2005–2030. World future scenarios show a significant increase in energy demand due to human development of less developed economies. For Spain, our research concluded that energy demand will remain stable in the next decade, despite the foreseen 2% annual growth of the nation’s economy. Despite the enormous energy concentration and density of fossil fuels, it will not be affordable to use them to supply energy demand in the future. The consolidation of renewable energies and increasing energy efficiency is the only way to satisfy the planet’s energy needs.

Linear regression long-term energy demand forecast modelling in Ogun State, Nigeria

This study uses linear regression analysis technique to forecast the energy load demand in Ogun State and the population for a 10-year period. Energy Demand data for the state for the years 2016 and 2017 was obtained from Ibadan Electricity Distribution Company (IBEDC) and the population data collected from the National Population Commission. The result shows a population growth rate of 3.35% per year. The Energy Consumption Demand should not rise above 1,365,024.72MWh for a population of 7,791,237 by the year 2028, showing an increase of 53% from 892,170.95MWh. The result of this work is very useful to energy distribution company (IBEDC) for their operational and planning activities for continued satisfactory service delivery.Keywords: Load forecasting, Energy Demand, Long-Term Forecast, Linear Regression

Regulatory Framework for Long-Term Demand Forecasting and Power Procurement Planning

Electricity supply chain is a capital intense activity requiring a long gestation period for setting up generation, transmission and distribution assets. A robust and stable regulatory and policy framework provides adequate incentive for investment in such capacities. Further, availability of adequate generation and transmission capacities in future needs to be ensured for reliable operation of the power system. A reliable electricity demand projection can not only help the distribution utilities to plan power procurement in advance, but also assist in creation of transmission infrastructure to ensure reliable electricity supply to consumers. Cost of power procurement is the most important element of the Aggregate Revenue Requirement (ARR) of distribution utilities and accounts for about three-fourth of the final consumer tariff. Power procurement plans and contracts typically have a long-term horizon and, therefore, need to be worked out well in advance, based on a reliable and dependable forecast. A national-level exercise for long-term electricity demand estimation is carried out by Central Electricity Authority (CEA), which projects state-wise long-term peak demand for electricity. The Electric Power Survey (EPS), published every five years by CEA, projects electricity demand using partial end use method, which is now supplemented with econometric estimates. Our analysis reveals that there is a deviation of up to 25 percent between the electricity demand projections in the EPS Reports and the actual demand of electricity. This mismatch can be attributed to quality of data as well as methodological shortcomings. Application of a standardised approach across states which vary in terms of economic development, consumption profile, agroclimatic conditions, etc. may also explain the range of deviations in demand projections across states. At the state level, the respective distribution (DISCOMs) and transmission licensees (TRANSCOs) also undertake such an exercise. The objectives, forecast horizon and methodology adopted for such forecasts vary widely. The objectives of some forecasts include transmission capacity planning, power procurement planning and/or regulatory compliance for tariff filings. While licensees in a few states revise their estimates annually, others undertake this activity every five years or more. CEA’s projections are adopted for transmission as well as generation planning in some states. A holistic sectoral approach combining long-term electricity demand forecast with power procurement would not only help utilities economise their overall cost of power procurement but also alleviate supply risks, especially with the growing share of renewable energy sources. We analysed historical demand projections and relevant regulations of selected states – Andhra Pradesh, Assam, Delhi, Gujarat, Madhya Pradesh, Maharashtra, Odisha, Punjab and Uttar Pradesh. Whereas some of the State Electricity Regulatory Commissions (SERCs) have issued specific regulations for demand forecasting and power procurement planning, others have covered only short-term aspects through tariff regulations. The prevailing regulatory framework of the sector across many states does not address the matter of long-term demand forecasting and power procurement planning in an integrated manner. Based on our analysis of different states and some international practices, we identified key aspects that should be considered in formulation of regulations for long-term demand forecasting and power procurement planning. SERCs can consider these key aspects, with appropriate modifications if necessary, while framing model regulations for long-term demand forecasting and power procurement planning.

A GIS-Based Integrated Model for Exploring Effects of Land Use Changes on Transport Demand: The Case of Dar es Salaam, Tanzania

Land use patterns essentially determine activity centre locations and opportunities for trip generation and attraction. Land uses signify the magnitude of travel demand and availability of transport patterns influences the future land uses. A number of literatures on land use and transport interactions show that the effects of land use change on transport appear to be more direct and stronger than the reverse. The main cause, in large part, is the important roles of trip generation and attraction, whose change of spatial distribution basically increases the number of trips and distances travelled between activity centres. Ignoring the mutual influences for the change between land use and transportation in long-term forecasting and planning can lead to decisions with unintended and undesirable consequences. This is more critical in developing countries cities where long-term travel demand forecasting and transportation infrastructure planning is seldom done as well as changes in land use are considerably high compared with the supply of transport infrastructure. This paper therefore reports upon a GIS-based integrated land use and transport model developed to determine effects of land use changes on travel demand. The model is developed based on vector map layers of the planned land uses, and changes obtained by overlaying the subsequent layers in ten-year intervals obtained from satellite images. The model is capable of performing a spatial-temporal analysis of land uses and illustrating effects on travel demand. The modelling system also supports analysis of policy measures that alternatively reduce the number of vehicle trips generated in the planned area.

Multivariate Statistical Analysis based Methodology for Long-Term Demand Forecasting

Forecasting models are necessaries in electrical utilities to set the energy cover for several years in order to minimize the operational cost. In this sense, a low error is required to avoid high levels energy exchange with market and hence an increase in the operation costs. However, demand energy has monthly a behavior correlated with some economics variables (e.g., gross domestic product, gold price), type and amount of days, population, historical demand, inter alia. For this reason, it is necessary to design a statistical methodology that allows selecting suitable variables and characterize them to establish which factors are significant for understanding. This paper proposes a methodology to identify those significant factors in the monthly forecasting model for energy demand in the power purchases area. The climatic scenario to increase the sensibility of the model, when the probability of the “Niño” or “Niña” phenomena increase month to month, was proposed.

Forecasting peak electricity demand for Los Angeles considering higher air temperatures due to climate change

Los Angeles County (LAC) is a large urbanized region with 9.7 million residents (as of 2010) and aging infrastructure. Population forecasts indicate that LAC will become home to an additional 1.2–3.1 million residents through 2060. Additionally, climate forecasts based upon representative concentration pathway (RCP) scenarios 4.5 and 8.5 indicate that average air temperatures will increase by 1–4 °C (2–7 °F) in the region. Both of these factors are expected to result in higher summertime peak electricity demand due to growth in the number of buildings, the percentage of installed air conditioners (ACs), and the additional cooling load on those air conditioners. In order to understand potential power reliability issues, and support infrastructure planning efforts, a long-term peak demand forecast was developed using hourly residential and commercial (R&C) building energy models. Peak hour electricity demand was estimated to increase from 9.5 to 12.8 GW for R&C sectors, to 13.0–17.3 GW (2–36%) and 14.7–19.2 GW (16–51%) by 2060 for the population forecasts from the California Department of Finance and the Southern California Association of Governments respectively. While marginal increases in ambient air temperature due to climate change accounted for only 4–8% of future increases in peak demand, differences in annual maximum temperatures within the 20-year periods affected results by 40–66% indicating a high sensitivity to heat waves. Population growth of at least 1 million people is anticipated to occur mostly in the northern cities of Palmdale, Lancaster, and Santa Clarita, bringing an additional 0.4–1 GW of peak demand in those regions. Building and AC efficiency are anticipated to improve as national and state efficiency standards increase, and as older, less efficient units are replaced; this could offset some of the projected increases in peak demand. Additionally, development of shared wall, multi-family dwelling units could enable population growth of up to 3 million people without increasing peak demand.

Disaggregating time series on multiple criteria for robust forecasting: The case of long-term electricity demand in Greece

Electricity demand forecasting is an essential process in the operation and planning procedures of power systems that considerably influences the decisions of utility providers. Main aim of this paper is, first, to examine the relationship between a time series and influential multiple criteria, and, second, to provide long-term electricity demand forecasts in Greece. An original disaggregation or ordinal regression analysis methodological framework is outlined to optimally assess a robust additive value model which is as consistent as possible with a given time series. The accuracy and stability of this modeling approach is guaranteed through the calculation of statistical error measures and robustness analysis indices, respectively. For the case of Greece, the additive value forecasting model is inferred from data related to the training period 1999–2013. The proposed method has been applied for the forecasting of the annual total net electricity demand in the Greek interconnected power system during the following testing period 2014–2016. The model implies that the level of economic growth, represented by the national gross domestic product, imposes the greatest influence on the electricity demand followed by the energy efficiency progress and the weather conditions in the country. The ordinal regression models perform considerably better than the multiple linear (least-squares) regression model, in terms of prediction reliability, resulting into a minimum MAPE equal to 0.74%. The exact method has been also applied for the extraction of electricity demand projections till 2027 based on alternative economic growth scenarios, indicating a constant increase of the electricity demand in Greece.

Long-term electricity demand forecast and supply side scenarios for Pakistan (2015–2050): A LEAP model application for policy analysis

Pakistan is facing electricity crises owing to lack of integrated energy planning, reliance on imported fuels for power generation, and poor governance. This situation has challenged governments for over a decade to address these crises. However, despite various conformist planning and policy initiatives, the balance between demand and supply of electricity is yet to be achieved. In this study, Long-range Energy Alternatives Planning System (LEAP) is used to develop Pakistan's LEAP modeling framework for the period 2015–2050. Following demand forecast, four supply side scenarios; Reference (REF), Renewable Energy Technologies (RET), Clean Coal Maximum (CCM) and Energy Efficiency and Conservation (EEC) are enacted considering resource potential, techno-economic parameters, and CO2 emissions. The model results estimate the demand forecast of 1706.3 TWh in 2050, at an annual average growth rate of 8.35%, which is 19 times higher than the base year demand. On the supply side, RET scenario, although capital-intensive earlier in the modeling period, is found to be the sustainable electricity generation path followed by EEC scenario with the lower demand of 1373.2 TWh and minimum Net Present Value (NPV) at an aggregate discount rate of 6%. Conclusion section of the paper provides the recommendations devised from this study results.

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan’s energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA), Holt-Winter and Long-range Energy Alternate Planning (LEAP) model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector’s demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16%) followed by natural gas (36.57%), electricity (16.22%), coal (7.52%) and LPG (1.52%) in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.

An Analyze of Long-term Hourly District Heat Demand Forecasting of a Commercial Building Using Neural Networks

With the building sector standing for a major part of the world's energy usage it of utmost importance to develop new ways of reduce the consumption in the sector. This paper discusses the evolution of the regulations and policies of the Swedish electric and district heating metering markets followed by the development of a nonlinear autoregressive neural network with external input (NARX), with the purpose of performing heat demand forecasts for a commercial building in Sweden. The model contains 13 input parameters including; calendar, weather, energy and social behavior parameters. The result revealed that these input parameters can predict the building heat demand to 96% accuracy on an hourly basis for the period of a whole year. Further analysis of the result indicates that the current data resolution of the district heat measuring system limits the future possibilities for services compared to the electric metering system. This is something to consider when new regulation and policies is formulated in the future.

Energy supply in Malawi: Options and issues

Inadequate energy supply is one of the major problems confronting Malawi and limiting its social, economic and industrial development. This paper reviews the current status of energy supply and demand in Malawi; examines the major sources of energy, current exploitation status and their potential contribution to the electricity supply of the country; discusses key issues facing the energy sector; and identifies broad strategies to be implemented to tackle the energy supply challenges. Using secondary data for its critical analysis, the paper also presents modelling of long-term energy demand forecast in the economic sectors of Malawi using the Model for Analysis of Energy Demand (MAED) for a study period from 2008-2030. Three scenarios namely reference (REF), moderate growth (MGS) and accelerated growth (AGS) were formulated to simulate possible future long-term energy demand based on socio-economic and technological development with the base year of 2008. Results from all scenarios suggest an increased energy demand in consuming sectors with biomass being a dominant energy form in household and industry sectors in the study period. Forecast results reveal that energy demand will increase at an annual growth rate of 1.2% and reach 5160 ktoe in 2030 under REF scenario. The growth rates for MGS and AGS are projected at 1.5% each reaching 4639 ktoe and 5974 ktoe in 2030, respectively. The final electricity demand of about 105 ktoe in the base year will grow annually at average rates of 13.8%, 15.3% and 12.6% for REF, AGS and MGS, respectively. Over the study period 2008-2030 the annual electricity per capita will increase from about 111 kWh to 1062, 1418 and 844 kWh for the REF, AGS and MGS, respectively. The final energy intensity will decrease continuously from about 13.71 kWh/US$ in the base year to 3.88 kWh/US$, 2.98 kWh/US$ and 5.27 kWh/US$ for the REF, AGS and MGS, respectively in the year 2030. In conclusion, the paper outlines strategies that could be utilized to ensure adequate supply of modern energy which is a key ingredient for achieving sustainable social and economic growth.

Hidden Markov Approach to Dynamically Modeling Car Ownership Behavior

It has become apparent to researchers in various domains that choice behavior occurs in a dynamic context and decision making involves strong temporal dependency, especially when it comes to car ownership decisions, because of consumers’ forward-looking behavior. However, a substantial portion of the literature focuses on static model formulations, and limitations exist, particularly in long-term travel demand forecasting. This study proposed a hidden Markov modeling (HMM) framework to analyze car ownership behavior dynamically. The dynamic model framework was applied to the 10-wave Puget Sound (Washington) Transportation Panel data. Two hidden states were identified in this study: State 1 tended to be land use entropy sensitive and vice versa for State 2. Empirical results reveal that households with preschool-age children are more likely to live in urbanized areas where they have easy access to various facilities. Also, one more licensed driver would lead to a 13.33% increase in owning two cars for State 1 households and a 28.45% increase in owning three or more cars for State 2 households. The comparison with both the multinomial logit model and the latent class model favors the study’s dynamic model framework with respect to model performance. The HMM approach offers insights on policy development for a target population and provides more accurate forecasting for long-term planning and policy analysis.

Long-Term Water Demand Forecasting for Sustainable Supply Planning

Long-Term Water Demand Forecasting for Sustainable Supply Planning

The analysis of dynamic travel mode choice: a heterogeneous hidden Markov approach

Discrete choices are often analyzed statically. The limitations of static models become more obvious when employing them in more long-term travel demand forecasting. The research gap lies in a theoretical model which is dynamically formulated, and in readily available longitudinal data sources. To address this, a heterogeneous hidden Markov modeling approach (HMM) is proposed in this paper to model dynamic discrete choices. Both longitudinal and cross-sectional heterogeneity are considered. The approach is demonstrated on a travel mode choice application using ten-wave Puget Sound Transport Panel data coupled with some other supplementary data sources. Results indicate that travelers’ long-term life-cycle stages have an enduring impact when shifted to different mode choice states, wherein sensitivities to travel time and cost vary. Empirical results are put in line with static discrete choice models. The paper demonstrates that the family of HMM models provide the best fitting model. The dynamic model has superior explanatory power in fitting longitudinal data and thus shall provide more accurate estimates for planning and policy analyses. The proposed approach can be generalized to study other short/mid-term travel behavior. The estimated model can be easily calibrated and transferred for applications elsewhere.

Long-term Electricity Demand Forecasting of Sumatera System Based on Electricity Consumption Intensity and Indonesia Population Projection 2010-2035

Abstract An important step in energy management especially in electricity planning is demand forecasting. A simple model is presented using LEAP (Long-range Energy Alternative Planning System) as a tool and Sumatera systems as a case study. It aims to be easy to understand and applicable. Electricity demand in household sector is calculated based on data of population, household size, electrification ratio and electricity intensity. On the other hand, electricity demand in non-household sector is calculated as a product of number of customers and its electricity intensity. The base year is 2010 and 2025 is the end of forecasting period. The result shows that electricity demand in Sumatera system would be increase more than seven times compared to base year value. A comparative study was also carried out.

Forecasting Long-term Electricity Demand in the Residential Sector

Abstract This work describes a methodology for long-term electricity demand forecast in the residential sector. The methodology has been used in the power market studies of some Brazilian distribution utilities. The methodology is based on decomposition of the total electricity residential consumption in three components: average consumption per consumer unit, electrification rate and number of households. Then, the forecast for the total electricity consumption in residential sector is the product of forecasts for these three components. The prediction for the number of households is based on demographic models while the future trajectory of the electrification rate is defined by the targets for achieving the universal access to electricity. The product of these two components provides a forecast to the number of residential customers. The average consumption per unit consumer depends on the macroeconomic scenarios for GDP, average household income and income distribution. The proposed methodology provides a framework to integrate macroeconomic scenario, demographic projection and assumptions for ownership and efficiency of electric appliances in a long-term demand forecast. In order to illustrate the application of the proposed methodology, this paper presents a ten-year demand forecasts for the residential sector in Brazil.