Annual Electricity and Energy Consumption Forecasting for the UK Based on Back Propagation Neural Network, Multiple Linear Regression, and Least Square Support Vector Machine
Abstract
:1. Introduction
References | Methods | Input Variables | Response Variables | Main Findings |
---|---|---|---|---|
Geem, Z. W. et al., 2009 [12] | ANN (momentum process, error backpropagation algorithm, feed-forward multilayer perceptron) | GDP, population, imports, exports | annual energy demand in South Korea | Energy demands reached a peak in specific years and then declined. The proposed model forecasted energy consumption greater than a linear regression model or an exponential model. |
Ekonomou, L., 2010 [13] | artificial neural networks (with multilayer perceptron model) | GDP, annual per capita electricity consumption, installed capacity, yearly environment temperature | annual energy consumption in Greece | The produced ANN results show better accuracy than an SVM method and a linear regression method for the years 2005–2008. |
Kandananond, K., 2011 [14] | multiple linear regression (MLR), ANN, ARIMA | population, GDP, income from the export of industrial products, stock index | annual electricity consumption in Thailand | The ANN model decreases the MAPE to 0.996%, while the MLR and ARIMA methods are 3.26% and 2.81%, respectively. |
Azadeh, A. et al., 2013 [15] | ANN (multi-layer perceptron) | GDP, oil price, gas price, carbon monoxide, carbon dioxide, nitrogen oxide, lagged variable | monthly renewable energy consumption in Iran | The suggested method is helpful for locations without measuring equipment and has more advantages than conventional and fuzzy regression models. |
Kialashaki, A., 2014 [16] | MLR, ANN | GDP, diesel fuel price, refiner price of propane, total energy consumption | annual energy demand in the American industry sector | The predictive result is consistent with the published forecast. |
Kaytez, et al., 2015 [17] | neural networks, regression analysis, minimum blocks SVM | installed power capacity, GDP, total population, and subscribership data in 1970–2009 | annual electricity consumption in Turkey | The proposed LS-SVM model shows accurate and rapid performance for forecasting. |
Aydin, G. et al., 2016 [18] | ANN | population, GDP, imports, exports | annual energy consumption in the highest energy consumption countries | The correlation coefficients between actual energy demands and the ANN model predictions are higher than 90%. |
Günay, M. E. et al., 2016 [19] | MLR, ANN | average summer and winter temperatures, population, GDP, inflation percentage, unemployment percentage | annual electricity demand in Turkey | Unemployment and average winter temperatures were negligible in determining the demand. The results are verified with high accuracy. |
Berriel, R. F. et al., 2017 [20] | convolutional and deep fully connected neural networks | standardized average energy demand based on past 12 months | monthly electricity consumption | The proposed model can forecast with a relative error of 17.29% and an absolute error of 31.83 kWh. |
Liu, B. et al., 2017 [21] | MLR, SVR, gated recurrent unit (GRU), ANN | GDP, population, imports, exports | annual primary energy consumption in China | The derived GRU model results indicate that China’s energy demand may change from 2954.04 Mtoe in 2015 to 5618.67 Mtoe in 2021. |
Pino-Mejías, R., 2017 [22] | linear regression models, ANN | equipment load, air flow rate, infiltration rate, lighting load, occupant load, intensity of use, indoor design condition, thermal inertia | cooling and heating energy demands, other energy | In the case of energy demand and CO2 emissions, the linear regression models that provide better capability are those in which the predictor variables have been converted. |
Zeng, Y. R. et al., 2017 [23] | BPNN model supported by adaptive differential evolution algorithms | GDP, population, imports, exports | annual electrical energy and total energy consumption | Compared with traditional anti-transmission neural network models and other common models, the ADE BPNN model can well forecast energy demand. |
Li, M. et al., 2018 [24] | grey prediction theory optimized by SVM algorithm | coal, coal power demand in previous years | annual energy demand and CO2 emissions in Chinese Beijing–Tianjin–Hebei region | It is estimated that, by 2030, electricity and natural gas energy consumption will reduce to 45.0%, and the carbon emissions can be controlled to less than 96.9 million tons. |
Mason, K. et al., 2018 [25] | neural network algorithm | historic and current values in the time series | monthly CO2 emissions, wind generation, and energy consumption in Ireland | The neural network is very competitive in predicting energy demand in Ireland, providing fast convergence, more robust and accurate forecasts. |
Al-Musaylh et al., 2019 [26] | ANN | six climate variables (vapor pressure, solar radiation, evaporation, rainfall, max and min temperature) and 51 reanalysis variables | 6 h and daily electricity consumption | The proposed neural network based on generic algorithm is more appropriate for short-term load predicting, and the proposed neural network predicts better for long term energy forecasting. |
Hamzaçebi, C., 2019 [27] | ANN | Turkey’s previous monthly electricity consumption | Turkey’s monthly electricity consumption | The ANN model makes successful and high-precision predictions of Turkey’s monthly electricity demand from 2015 to 2018. |
Laib, O. et al., 2019 [28] | Long-short-term memory recurrence neural network | temperature | daily and hourly natural gas consumption in Algeria | The results are compared with three methods: LSTM, MP neural network method, and seasonal time series of out-of-source variable models. |
Zagrebina, S. et al., 2019 [29] | recurrent neural network | data of production calendars, meteorological factors (precipitation, clouds, wind speed, temperature, daylight length, etc.) | daily electricity consumption in Southern and Siberian Federal Districts in Russia | The recurrent neural network is constructed to produce more accurate prediction results. |
AJ del Real. et al., 2020 [30] | a hybrid architecture consisting of an ANN and a sink neural network. | predicted weather data | monthly electricity demand of the French Grid | The solution gets the highest performance score among traditional ANN models and automatic decreasing integrated moving averages (ARIMA). |
2. Methods
2.1. Back Propagation Neural Network
2.2. Multiple Linear Regression
2.3. Least Square Support Vector Machine
3. Application
3.1. Data
3.2. Performance Criterions
4. Results and Discussion
4.1. Results of BP-NN Model
4.2. Results of MLR Model
4.3. Results of LS-SVM Model
4.4. Comparison of Three Models
- (1)
- The original data on electricity and energy consumption do not vary consistently. The consumption data first increases and then decreases. For the training set, the increasing trend points account for the vast majority, and the declining trend points account for little, which might contribute to the overestimation on the test set.
- (2)
- GDP is strongly correlated with electricity and energy consumption before 2007, but the correlation weakens, speculated as the fact that the GDP varies with fluctuations but generally remains unchanged, while the electricity and energy consumption decrease year by year after 2007. Though the economic development becomes healthy and green, the forecasting for electricity and energy consumption gets tough when GDP is used as one of the most important input variables for the model. Therefore, more correlated economic indicators should be introduced into the input variables.
- (3)
- The total data is extracted from 1993 to 2019, and this relatively small amount of data could not train a perfect model to forecast annual national electricity and energy consumption.
- (1)
- The data in the training set should have a similar variation tendency to the total dataset.
- (2)
- More economic indicators correlated with energy consumption should be introduced into the input variables for the forecasting model.
- (3)
- More data should be collected, and the starting year of the dataset extended to an earlier date.
4.5. Prediction of Future Data
5. Conclusions
- (1)
- The weather data are negligible in determining the national annual electricity and energy consumption.
- (2)
- Electricity and energy consumption have a strong correlation with GDP before 2007, but the correlation weakens after 2007, indicating that a greener economic pattern is generated and developed.
- (3)
- The LS-SVM model shows better forecasting performance than the BP-NN and MLR models in the training set.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Year | Population (Millions) | GDP (USD) | Mean Temperature (°C) | Sunshine (Hours) | Rainfall (mm) | Air Frost (Days) | Electricity Consumption (TWh) | Energy Consumption (Million Tons) |
---|---|---|---|---|---|---|---|---|
1993 | 57.7 | 1.06 × 1012 | 8.33 | 1211 | 1149.7 | 56 | 295.75 | 220.73 |
1994 | 57.9 | 1.14 × 1012 | 8.89 | 1358.8 | 1216.5 | 48.1 | 292.83 | 217.47 |
1995 | 58 | 1.35 × 1012 | 9.17 | 1579.5 | 1050.3 | 65.5 | 303.92 | 218.39 |
1996 | 58.2 | 1.42 × 1012 | 8.18 | 1389.7 | 935.2 | 77 | 319.78 | 229.99 |
1997 | 58.3 | 1.56 × 1012 | 9.41 | 1420 | 1048.7 | 49.4 | 321.07 | 226.81 |
1998 | 58.5 | 1.65 × 1012 | 9.16 | 1258 | 1298.5 | 46.8 | 325.35 | 230.74 |
1999 | 58.7 | 1.69 × 1012 | 9.37 | 1406.7 | 1271.8 | 43.8 | 332.05 | 231.33 |
2000 | 58.9 | 1.66 × 1012 | 9.1 | 1358.2 | 1372.5 | 43.2 | 340.30 | 234.81 |
2001 | 59.1 | 1.64 × 1012 | 8.8 | 1406.7 | 1049.4 | 70.7 | 342.50 | 236.85 |
2002 | 59.4 | 1.78 × 1012 | 9.44 | 1297.2 | 1280.4 | 36.4 | 344.11 | 229.60 |
2003 | 59.6 | 2.06 × 1012 | 9.47 | 1586.3 | 900.1 | 60.2 | 346.62 | 231.87 |
2004 | 60 | 2.42 × 1012 | 9.44 | 1355.5 | 1208.9 | 46.6 | 347.71 | 233.63 |
2005 | 60.4 | 2.54 × 1012 | 9.42 | 1381.9 | 1079.6 | 53.6 | 357.20 | 236.29 |
2006 | 60.8 | 2.72 × 1012 | 9.7 | 1472.3 | 1173.2 | 51.7 | 353.86 | 233.07 |
2007 | 61.3 | 3.11 × 1012 | 9.56 | 1436.1 | 1195.7 | 39.2 | 351.45 | 227.49 |
2008 | 61.8 | 2.94 × 1012 | 9.02 | 1375.2 | 1293.2 | 57.9 | 349.53 | 225.67 |
2009 | 62.3 | 2.43 × 1012 | 9.14 | 1465.7 | 1208.1 | 56.9 | 329.42 | 211.68 |
2010 | 62.8 | 2.49 × 1012 | 7.94 | 1444.5 | 945.4 | 93.4 | 337.51 | 219.31 |
2011 | 63.3 | 2.67 × 1012 | 9.61 | 1398 | 1162.6 | 41.3 | 325.92 | 203.59 |
2012 | 63.7 | 2.72 × 1012 | 8.74 | 1332.1 | 1329.7 | 54.9 | 325.48 | 207.80 |
2013 | 64.1 | 2.80 × 1012 | 8.74 | 1410.9 | 1084 | 71.1 | 324.38 | 206.26 |
2014 | 64.6 | 3.09 × 1012 | 9.88 | 1416.6 | 1292.8 | 31.7 | 310.80 | 193.60 |
2015 | 65.1 | 2.96 × 1012 | 9.18 | 1445 | 1265.3 | 43.9 | 311.42 | 195.05 |
2016 | 65.6 | 2.72 × 1012 | 9.29 | 1417.4 | 1096.7 | 54 | 311.30 | 192.46 |
2017 | 66 | 2.70 × 1012 | 9.53 | 1369.3 | 1118.5 | 48.3 | 306.79 | 190.62 |
2018 | 66.4 | 2.90 × 1012 | 9.45 | 1560.1 | 1053.6 | 54 | 307.37 | 189.52 |
2019 | 66.8 | 2.88 × 1012 | 9.39 | 1454.4 | 1232.3 | 48.1 | 302.50 | 184.48 |
2020 | 67.2 | 2.76 × 1012 | 9.62 | 1497.4 | 1336.3 | 37.7 |
2020 | Actual Value | BP-NN | MLR | LS-SVM |
---|---|---|---|---|
Electricity consumption (TWh) | 287.47 | 344.68 | 319.34 | 321.25 |
Energy consumption (Million tons) | 162.51 | 228.34 | 203.34 | 206.7 |
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Liu, Y.; Li, J. Annual Electricity and Energy Consumption Forecasting for the UK Based on Back Propagation Neural Network, Multiple Linear Regression, and Least Square Support Vector Machine. Processes 2023, 11, 44. https://doi.org/10.3390/pr11010044
Liu Y, Li J. Annual Electricity and Energy Consumption Forecasting for the UK Based on Back Propagation Neural Network, Multiple Linear Regression, and Least Square Support Vector Machine. Processes. 2023; 11(1):44. https://doi.org/10.3390/pr11010044
Chicago/Turabian StyleLiu, Yinlong, and Jinze Li. 2023. "Annual Electricity and Energy Consumption Forecasting for the UK Based on Back Propagation Neural Network, Multiple Linear Regression, and Least Square Support Vector Machine" Processes 11, no. 1: 44. https://doi.org/10.3390/pr11010044
APA StyleLiu, Y., & Li, J. (2023). Annual Electricity and Energy Consumption Forecasting for the UK Based on Back Propagation Neural Network, Multiple Linear Regression, and Least Square Support Vector Machine. Processes, 11(1), 44. https://doi.org/10.3390/pr11010044