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

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## Abstract

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## 1. Introduction

## 2. Importance of Load Forecasting for Pakistan—A Global Perspective

## 3. Forecasting Methodologies—Models and Techniques

- Short term load forecasting (STLF)
- Medium term load forecasting (MTLF)
- Long term load forecasting (LTLF)

#### 3.1. Bottom-Up Models

#### Long Range Energy Alternatives Planning (LEAP)

_{2}emissions, were compared in terms of economic and environmental aspects. Another LEAP model is applied by devising BAU, NC, and GF as three different scenarios [40]. In China, five important scenarios to predict the future electricity supply and demand have been developed [41]. Simplistic, yet comprehensive, grounds were discovered to analyze a framework incorporating the possible future scenarios for the energy demand and CO

_{2}projections in Colombia [42]. Using three different scenarios, the authors drew a comparison with the government’s power sector expansion plans in Pakistan [43]. By projecting the future gaseous emissions in Pakistan, recommendations to the government were made to include more renewable energy resources and lessen oil and fossil fuel imports.

_{2}emissions, rendering the region a low carbon economy. In transportation domains as well, a LEAP model is used to monitor the growing energy demand in two cities of Pakistan [47].

#### 3.2. Top-Down Models

#### Econometric Forecast Models

#### 3.3. Regression Analysis

_{t}—measured values for load, t—sampling time, v

_{t}—vector of demand determinants and e

_{t}—model error.

#### 3.4. Time Series Forecasting Techniques

#### 3.4.1. ARMA/ARIMA/SARIMA

#### 3.4.2. Exponential Smoothing

#### 3.4.3. Some Additional Time Series Techniques

#### 3.5. Artificial Intelligence-Based Techniques

_{k}—first order difference and A

_{k}—second order difference.

#### 3.6. Additive Models

_{k}—parametrically specified function/non-parametrically specified smooth function and x

_{kt}—kth component of vector xt where $k=1,\dots ,p$.

## 4. Electricity Demand Forecasting Methodologies and Its Determinants—A Comparative Analysis

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Global research trends in electricity load forecasting since the year 2000 [3].

**Figure 2.**Methodology: (

**a**) the country selection process for the comparison with Pakistan; (

**b**) the article selection process.

**Figure 3.**Selection of countries based on their gross domestic product (GDP), access to electricity, kWh/capita, and CO

_{2}emissions.

**Figure 9.**The usage of forecasting methodologies over different time horizons in low and middle income countries (LMICs).

**Table 1.**Load forecasting applications over different time horizons [31].

Application | STLF | MTLF | LTLF |
---|---|---|---|

Energy purchasing | Yes | Yes | Yes |

Transmission & distribution (T&D) planning | No | Yes | Yes |

Operations | Yes | No | No |

DSM | Yes | Yes | Yes |

Financial planning | No | Yes | Yes |

Forecast Horizon | Country of Study | Forecasting Methodologies | Demand Determinants Involved | Study Year | Forecast Period | Ref. No. |
---|---|---|---|---|---|---|

LTLF | Colombia | Long-range Energy Alternative Planning System (LEAP) | GDP, number of households, national vehicle fleet (vehicles per household) | 2018 | 2015–2030 & 2015–2050 | [42] |

LTLF | China (Beijing) | Long-range Energy Alternative Planning System (LEAP) | Terminal energy needs, activity levels, energy intensity, departmental activity, terminal activity, energy equipment activity level, environmental emission, environmental emission factor | 2019 | 2017–2035 | [41] |

LTLF | Turkey | Cointegration and ARIMA | Price, GDP per capita, consumption per capita | 2007 | 2005–2014 | [76] |

LTLF | South Africa | ARMA, Neural networks and Neuro-fuzzy systems | Previous consumption data | 2014 | 1985–2011 | [67] |

LTLF | Pakistan | Bottom up Approaches (LEAP Model) | Population, GDP, Electricity consumption per capita, GHG emissions | 2019 | 2015–2035 | [43] |

LTLF | Pakistan | GDP, GDP growth, Population, Population growth, energy intensity growth rate | 2015 | 2011–2030 | [37] | |

LTLF | Pakistan | income, income growth rate, population, and population growth rate, number of households, household size and GDP | 2018 | 2016–2040 | [46] | |

LTLF | Pakistan | GDP growth trend, electricity consumers growth, fuel cost, Technology’s lifetime, Plant Capacity Factor | 2018 | 2015–2050 | [39] | |

LTLF | Pakistan | Electric consumer growth, level of activities (number of consumers), final energy intensity (energy consumed per consumer), forecasted growth and other factors. | 2014 | 2011–2030 | [40] | |

LTLF | Pakistan | Rate of urbanization, penetration of energy efficient devices, population growth control plan, economic growth, domestic consumption trends. Income growth. | 2012 | 2005–2030 | [44] | |

LTLF | Pakistan | ARIMA (3,1,2), SARIMA (2, 1, 2), SMA (12), ARCH (2), GARCH (1,1) | Socioeconomic Factors, Seasonal Variations. | 2014 | 1990–2011 | [73] |

LTLF | Pakistan | ARIMA, Holt-Winter, LEAP | Not Discussed | 2017 | 2015–2035 | [72] |

LTLF | Pakistan | Functional Time series (FTS) | Seasonal variations, economic growth, urbanization, population growth, industrialization | 2015 | 2012–2021 | [95] |

LTLF | Pakistan | Ordinary Least Square technique using MDEE Model & multiple linear regression Index Model | GDP, population, electricity price, previous year’s electricity demand, and the number of consumers | 2012 | 2007–2030 | [62] |

LTLF | China | Trigonometric grey prediction approach | Not discussed | 2006 | 1981–2001 | [93] |

LTLF | India | Grey-Markov, Grey | Not discussed | 2009 | 2005–2015 | [91] |

LTLF | Turkey | structural time series analysis | Electricity prices | 2011 | 2010–2020 | [94] |

LTLF | China | Econometric Model and System Dynamic Approach | Internet age, marketization reform, technological progress and consciousness of energy conservation and emission reduction. | 2017 | 2000–2050 | [52] |

LTLF | Brazil | Spatial econometric approach using ARIMA model | Spatial Data, Past consumption, GDP, and population | 2017 | Not Provided | [51] |

LTLF | Thailand | MLR/ANN | GDP, population | 2015 | 2010, 2015,2020 | [63] |

LTLF | India | Regression Techniques | Net State Domestic Product (NSDP), Sector-wise Domestic Savings Household sector, Consumers, Connected Load | 2019 | 2006–2012 | [59] |

LTLF | Philippines | Multiple Linear Regression | Historical data, number of consumers for past 5 years, development plans (commercial, industrial etc.) for next 10 years | 2017 | 2016–2025 | [57] |

LTLF | China | Hybrid self-adaptive Particle Swarm Optimization–Genetic Algorithm–Radial Basis Function | GDP, population, industrial energy intensity, average annual temperature | 2014 | 2013–2020 | [100] |

LTLF | India | K-mean clustering and ANN | Load data and population | 2018 | 2018–2026 | [107] |

LTLF | India | ANN-PSO Models | Population, consumers, per capita income/GDP | 2017 | 2001–2015 | [106] |

LTLF | Thailand | Genetic Programming and Simulated Annealing (GSA) Model | Population, gross domestic product (GDP), stock index, and total revenue from exporting industrial products | 2013 | 2004–2009 | [114] |

LTLF | Thailand | ANN, ARIMA, MLR | Population, stock exchange index, GDP and amount of export | 2011 | 1986–2010 | [113] |

LTLF | Turkey | ANN | Population, GDP per capita, inflation percentage, unemployment percentage, average temperature | 2015 | 2014–2028 | [115] |

LTLF | Pakistan | STAR (Smooth Transition Auto-Regressive) Model | GDP per capita, Electricity Prices | 2014 | 1971–2012 | [64] |

LTLF | Brazil | Bottom-Up Approach | Previous load data (1995–2015), electric consumption by process, value added of different sectors, electricity price, production and value addition forecasts until 2050 | 2017 | 2015–2050 | [33] |

LTLF | Pakistan | Winter Holt and ARIMA | Consumption Sectors: Household, Govt. Sector, Street Lights, Commercial, Industrial, Agriculture. | 2015 | 2012–2020 | [74] |

LTLF | Pakistan | Univariate Time Series Model, Multiple Linear Regression based Econometric Model | GDP, Income per capita and Population | 2011 | 2011–2025 | [49] |

LTLF | Pakistan | ARIMA | Hydroelectricity consumption data, GDP, population growth rates | 2020 | 2018–2030 | [75] |

LTLF | Russian Federation | Regression Analysis AND Econometric Models | Elasticity of GDP, electricity intensity, GDP growth rate, income growth rate, electricity prices | 2009 | N/A | [48] |

LTLF | Venezuela | Econometric Model | GDP, Electricity price, number of consumers, aluminum and iron price, population. | 2006 | 2004–2024 | [50] |

LTLF | Mexico | Multiple Linear Regression | Number of establishments, number of employees, number of shipments, electricity prices, natural gas prices | 2004 | 2002–2010 | [58] |

LTLF | Islamic Republic of Iran | ANN and FL | GDP, GDP without accounting for oil, (GNP), Iranian oil price, value added of manufacturing and mining group, oil income, population consumer price index, gas consumption, electricity, water and gas supply, exchange rate, gold price | 2008 | 2008–2011 | [111] |

MTLF | China | Residual Modification of SARIMA | GDP, generation | 2012 | Apr2010–Sept 2010, 2011–2013 | [78] |

MTLF | India | MSARIMA | Previous loads | 2012 | April 2010–March 2011 | [84] |

MTLF | Malaysia | Holt-Winters Taylor (HWT), Holt-Winters, modified Holt-Winters exponential smoothing | Previous load data, seasonal patterns | 2013 | 2005–2006 | [90] |

MTLF | Thailand | MLR/ARIMA | Seasonal weather, national economic growth, monthly peak load | 2006 | 2006–2007 | [126] |

MTLF | Turkey | Seasonal ANN | Load data and weather | 2017 | Monthly forecasts between 2015 to 2018 | [116] |

MTLF | China | SVR (support vector regression) with chaotic artificial bee colony algorithm | Past Load Data | 2011 | Monthly forecasts from Oct 2008 to April 2009 | [127] |

MTLF | South Africa | Generalized Additive Model | Temperature and load data | 2017 | Monthly predictions | [125] |

MTLF | China | Semiparametric-based additive model | Meteorological and economic variables | 2014 | Monthly predictions between 2006–2011 | [124] |

MTLF | Russian Federation | ANN (Caterpillar-SSA Method) | Load data, calendar effects (days, week, month, years) | 2017 | - | [117] |

MTLF | Venezuela | Singular Spectrum Analysis of Time Series Data | Load data | 2013 | - | [92] |

STLF | Islamic Republic of Iran | ARIMA | Load data, temperature data | 2001 | [79] | |

STLF | Pakistan | ANN & Bagged Regression Tree | Weather, time factor, past load data | 2018 | [118] | |

STLF | South Africa | SARIMA, SARIMA-GARCH, Reg-SARIMA-GARCH | Previous consumption data, Seasonality, Day of the week, month, year | 2011 | [77] | |

STLF | South Africa | Regression-SARIMA | Previous consumption data | 2012 | [60] | |

STLF | China | Hybrid of ARIMA and SVMs | Previous loads, day of the week, weather | 2012 | [81] | |

STLF | China | ARIMA-ANN | Previous loads | 2004 | [82] | |

STLF | Malaysia | Double SARIMA | Previous load data | 2010 | [83] | |

STLF | Malaysia | Holt-Winters Taylor (HWT), Holt-Winters, modified Holt-Winters exponential smoothing | Previous load data, seasonal patterns | 2013 | [90] | |

STLF | Indonesia | Multiple Linear Regression | Historical data, temperature data | 2007 | Forecast periods for STLF are diverse and reveal no significant information. Therefore, these time periods are not provided. | [56] |

STLF | China | Decreasing step fruit fly optimization algorithm | Historical data, weather/temperature data | 2017 | [99] | |

STLF | Colombia | ANN | Historical load data | 2015 | [101] | |

STLF | India | FL and WNN | Past load data | 2014 | [105] | |

STLF | Indonesia | Singular spectrum analysis, fuzzy systems and neural networks | Load data | 2019 | [108] | |

STLF | Malaysia | ANN | Load data | 2010 | [110] | |

STLF | Philippine | Fast ANN | Load data, day timings (day of week, week of month) | 2015 | [128] | |

STLF | South Africa | Adaptive Neuro Fuzzy Inference System—ANFIS | Temperature, humidity, load data | 2010 | [112] | |

STLF | Pakistan | XGBoost Algorithm using NN | Load data, weather data | 2019 | [119] | |

STLF | Argentina | Radial Basis Function Neural Network, and Feed-forward Neural Network, Multi-Linear Regression | Load data, weather data (temperature etc.), days of the week/month | 2017 | [103] | |

STLF | Russian Federation | Long short-term memory ANN, SVM regression based on radial basis functions (RBF) SVM Regression linear and ARIMA. | Load Data | 2019 | [104] | |

STLF | Russian Federation | ANN | Load Data, calendar effects, temperature, wind speed | 2018 | [109] | |

STLF | Islamic Republic of Iran | ANN | Load data | 2008 | [102] | |

STLF | Islamic Republic of Iran | Singular Spectrum Analysis of Time Series Data | Load data | 2011 | [96] |

Electricity Demand Determinants | Frequency of Occurrence | %Age Usage Over Total Studies Reviewed |
---|---|---|

GDP/Economic growth | 20 | 29.0 |

GDP growth rate | 3 | 4.3 |

GDP/capita | 3 | 4.3 |

Population | 15 | 21.7 |

Population growth rate | 5 | 7.2 |

Consumption per capita/Energy intensity | 4 | 5.8 |

Energy intensity/Energy intensity growth rate | 6 | 8.7 |

Income/Income growth rate/Income per capita | 6 | 8.7 |

Weather (temperature, humidity, rain levels etc.) | 21 | 30.4 |

Electricity prices | 8 | 11.6 |

Number of consumers/consumer growth rate | 7 | 10.1 |

Previous load data | 37 | 53.6 |

Household size/household growth rate/number of households | 2 | 2.9 |

Urbanization | 2 | 2.9 |

Stock exchange index | 2 | 2.9 |

Spatial data | 1 | 1.4 |

Socioeconomic factors | 1 | 1.4 |

Energy conservation | 1 | 1.4 |

Device or appliance efficiency | 1 | 1.4 |

Industrial development | 5 | 7.2 |

Calendar Effects | 4 | 5.7 |

Country Name | Variables Most Frequently Used in Forecast Models |
---|---|

China | Terminal energy needs, energy intensity, environmental emission factors, technological progress, energy conservation, GDP, population, weather variables, generation, load data, calendar effects, |

India | Net State Domestic Product, sector-wise domestic savings, consumers, connected load, load data, population, consumers, per capita income/GDP |

Pakistan | Population, GDP, per capita consumption, GHG emissions, energy intensity growth rate, income related variables, household variables, electricity consumers growth, fuel cost, technology’s lifetime, plant capacity factor, urbanization rate, energy efficiency, population growth control plan, electricity price, hydroelectricity consumption data, weather variables |

Islamic Republic of Iran | GDP, Iranian oil price, value added of manufacturing and mining group, oil income, population consumer price index, gas consumption, electricity, water and gas supply, exchange rate, gold price, load data, weather data |

Russian Federation | Elasticity of GDP, electricity intensity, GDP growth rate, income variables, electricity prices, load data, calendar effects, weather variables. |

Colombia | GDP, household variables, national vehicle fleet, load data |

Turkey | Electricity price, GDP per capita, electricity consumption per capita, load data, weather variables, population, inflation percentage, unemployment percentage, weather variables. |

South Africa | Load data, weather variables, calendar effects |

Argentina | Load data, weather variables, calendar effects |

Philippine | Load data, calendar effects, number of consumers, development plans. |

Thailand | Population, GDP, stock index, total revenue from exporting industrial products, stock exchange index, weather variables, load data |

Venezuela | Load data |

Malaysia | Load data, calendar effects |

Indonesia | Load data, weather variables |

Mexico | Number of establishments, number of employees, number of shipments, electricity prices, natural gas prices |

Brazil | Load data, electric consumption by process, value added of different sectors, electricity price, production and value addition forecasts, spatial data, GDP, and population |

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**MDPI and ACS Style**

Mir, A.A.; Alghassab, M.; Ullah, K.; Khan, Z.A.; Lu, Y.; Imran, M.
A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons. *Sustainability* **2020**, *12*, 5931.
https://doi.org/10.3390/su12155931

**AMA Style**

Mir AA, Alghassab M, Ullah K, Khan ZA, Lu Y, Imran M.
A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons. *Sustainability*. 2020; 12(15):5931.
https://doi.org/10.3390/su12155931

**Chicago/Turabian Style**

Mir, Aneeque A., Mohammed Alghassab, Kafait Ullah, Zafar A. Khan, Yuehong Lu, and Muhammad Imran.
2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons" *Sustainability* 12, no. 15: 5931.
https://doi.org/10.3390/su12155931