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Journal articles on the topic 'Energy consumption – Ontario – Forecasting'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Shirzadi, Navid, Ameer Nizami, Mohammadali Khazen, and Mazdak Nik-Bakht. "Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning." Designs 5, no. 2 (April 6, 2021): 27. http://dx.doi.org/10.3390/designs5020027.

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Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has become a crucial need. Such forecasts are necessary to support the plans and decisions related to the capacity evaluation of centralized and decentralized power generation systems, demand response strategies, and controlling the operation. To address this problem, the main objective of this study is to develop and compare precise district level models for predicting the electrical load demand based on machine learning techniques including support vector machine (SVM) and Random Forest (RF), and deep learning methods such as non-linear auto-regressive exogenous (NARX) neural network and recurrent neural networks (Long Short-Term Memory—LSTM). A dataset including nine years of historical load demand for Bruce County, Ontario, Canada, fused with the climatic information (temperature and wind speed) are used to train the models after completing the preprocessing and cleaning stages. The results show that by employing deep learning, the model could predict the load demand more accurately than SVM and RF, with an R-Squared of about 0.93–0.96 and Mean Absolute Percentage Error (MAPE) of about 4–10%. The model can be used not only by the municipalities as well as utility companies and power distributors in the management and expansion of electricity grids; but also by the households to make decisions on the adoption of home- and district-scale renewable energy technologies.

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Sultana, Nahid, S. M. Zakir Hossain, Salma Hamad Almuhaini, and Dilek Düştegör. "Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand." Energies 15, no. 9 (May 7, 2022): 3425. http://dx.doi.org/10.3390/en15093425.

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This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1~6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73~2.98%; Summer: 8.41~14.44%). The coefficient of determination (R2) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts.

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Chen, Jack, Kerry Anderson, Radenko Pavlovic, Michael D. Moran, Peter Englefield, Dan K. Thompson, Rodrigo Munoz-Alpizar, and Hugo Landry. "The FireWork v2.0 air quality forecast system with biomass burning emissions from the Canadian Forest Fire Emissions Prediction System v2.03." Geoscientific Model Development 12, no. 7 (July 26, 2019): 3283–310. http://dx.doi.org/10.5194/gmd-12-3283-2019.

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Abstract. Biomass burning activities can produce large quantities of smoke and result in adverse air quality conditions in regional environments. In Canada, the Environment and Climate Change Canada (ECCC) operational FireWork (v1.0) air quality forecast system incorporates near-real-time biomass burning emissions to forecast smoke plumes from fire events. The system is based on the ECCC operational Regional Air Quality Deterministic Prediction System (RAQDPS) augmented with near-real-time wildfire emissions using inputs from the Canadian Forest Service (CFS) Canadian Wildland Fire Information System (CWFIS). Recent improvements to the representation of fire behaviour and fire emissions have been incorporated into the CFS Canadian Forest Fire Emissions Prediction System (CFFEPS) v2.03. This is a bottom-up system linked to CWFIS in which hourly changes in biomass fuel consumption are parameterized with hourly forecasted meteorology at fire locations. CFFEPS has now also been connected to FireWork. In addition, a plume-rise parameterization based on fire-energy thermodynamics is used to define the smoke injection height and the distribution of emissions within a model vertical column. The new system, FireWork v2.0 (FireWork–CFFEPS), has been evaluated over North America for July–September 2017 and June–August 2018, which are both periods when western Canada experienced historical levels of fire activity with poor air quality conditions in several cities as well as other fires affecting northern Canada and Ontario. Forecast results were evaluated against hourly surface measurements for the three pollutant species used to calculate the Canadian Air Quality Health Index (AQHI), namely PM2.5, O3, and NO2, and benchmarked against the operational FireWork v1.0 system (FireWork-Ops). This comparison shows improved forecast performance and predictive skills for the FireWork–CFFEPS system. Modelled fire-plume injection heights from CFFEPS based on fire-energy thermodynamics show higher plume injection heights and larger variability. The changes in predicted fire emissions and injection height reduced the consistent over-predictions of PM2.5 and O3 seen in FireWork-Ops. On the other hand, there were minimal fire emission contributions to surface NO2, and results from FireWork–CFFEPS do not degrade NO2 forecast skill compared to the RAQDPS. Model performance statistics are slightly better for Canada than for the US, with lower errors and biases. The new system is still unable to capture the hourly variability of the observed values for PM2.5, but it captured the observed hourly variability for O3 concentration adequately. FireWork–CFFEPS also improves upon FireWork-Ops categorical scores for forecasting the occurrence of elevated air pollutant concentrations in terms of false alarm ratio (FAR) and critical success index (CSI).

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Karatzoglidi, Mary, Paraskevas Kerasiotis, and Verena Kantere. "Automated energy consumption forecasting with EnForce." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2771–74. http://dx.doi.org/10.14778/3476311.3476341.

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The need to reduce energy consumption on a global scale has been of high importance during the last years. Research has created methods to make highly accurate forecasts on the energy consumption of buildings and there have been efforts towards the provision of automated forecasting for time series prediction problems. EnForce is a novel system that provides fully automatic forecasting on time series data, referring to the energy consumption of buildings. It uses statistical techniques and deep learning methods to make predictions on univariate or multivariate time series data, so that exogenous factors, such as outside temperature, are taken into account. Moreover, the proposed system provides automatic data preprocessing and, therefore, handles noisy data, with missing values and outliers. EnForce includes full API support and can be used both by experts and non-experts. The proposed demonstration showcases the advantages and technical features of EnForce.

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5

Pegalajar, M. C., and L. G. B. Ruiz. "Time Series Forecasting for Energy Consumption." Energies 15, no. 3 (January 21, 2022): 773. http://dx.doi.org/10.3390/en15030773.

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Introduction In the last few years, there has been considerable progress in time series forecasting algorithms, which are becoming more and more accurate, and their applications are numerous and varied [...]

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6

Degefa, Mehari Weldemariam. "Ethiopian energy consumption forecast." Mehran University Research Journal of Engineering and Technology 41, no. 4 (October 1, 2022): 42. http://dx.doi.org/10.22581/muet1982.2204.04.

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This Energy consumption forecast is vital and has a great economic impact. Mathematical models developed for energy forecast can also serve as inputs for further studies. This study is intended to develop an energy consumption forecast using the grey prediction model GM (1,1), based on the actual energy consumption data from the year 2008 to 2017. The models are developed for the total, solid biomass, oil products, and electrical energy consumption; and the accuracy for each model is ratified. These developed forecasting models were used to anticipate six-year Ethiopian consumption of major energy types. The outcomes of models for all four energy consumption types show an upward trend; simulating and forecasting are found suited with the grey system model with development coefficient values less than 0.3 for all selected energy forms.

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7

Han, Sun, Zhang Xianfeng, and Guo Haixiang. "China’s Energy Consumption Demand Forecasting and Analysis." Journal of Applied Sciences 13, no. 21 (October 15, 2013): 4912–15. http://dx.doi.org/10.3923/jas.2013.4912.4915.

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8

Ma, Jie, Amos Oppong, Kingsley Nketia Acheampong, and Lucille Aba Abruquah. "Forecasting Renewable Energy Consumption under Zero Assumptions." Sustainability 10, no. 3 (February 25, 2018): 576. http://dx.doi.org/10.3390/su10030576.

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9

Tomčala, Jiří. "Towards Optimal Supercomputer Energy Consumption Forecasting Method." Mathematics 9, no. 21 (October 23, 2021): 2695. http://dx.doi.org/10.3390/math9212695.

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Accurate prediction methods are generally very computationally intensive, so they take a long time. Quick prediction methods, on the other hand, are not very accurate. Is it possible to design a prediction method that is both accurate and fast? In this paper, a new prediction method is proposed, based on the so-called random time-delay patterns, named the RTDP method. Using these random time-delay patterns, this method looks for the most important parts of the time series’ previous evolution, and uses them to predict its future development. When comparing the supercomputer infrastructure power consumption prediction with other commonly used prediction methods, this newly proposed RTDP method proved to be the most accurate and the second fastest.

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Pavlicko, Michal, Mária Vojteková, and Oľga Blažeková. "Forecasting of Electrical Energy Consumption in Slovakia." Mathematics 10, no. 4 (February 12, 2022): 577. http://dx.doi.org/10.3390/math10040577.

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Prediction of electricity energy consumption plays a crucial role in the electric power industry. Accurate forecasting is essential for electricity supply policies. A characteristic feature of electrical energy is the need to ensure a constant balance between consumption and electricity production, whereas electricity cannot be stored in significant quantities, nor is it easy to transport. Electricity consumption generally has a stochastic behavior that makes it hard to predict. The main goal of this study is to propose the forecasting models to predict the maximum hourly electricity consumption per day that is more accurate than the official load prediction of the Slovak Distribution Company. Different models are proposed and compared. The first model group is based on the transverse set of Grey models and Nonlinear Grey Bernoulli models and the second approach is based on a multi-layer feed-forward back-propagation network. Moreover, a new potential hybrid model combining these different approaches is used to forecast the maximum hourly electricity consumption per day. Various performance metrics are adopted to evaluate the performance and effectiveness of models. All the proposed models achieved more accurate predictions than the official load prediction, while the hybrid model offered the best results according to performance metrics and supported the legitimacy of this research.

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11

Amber, Khuram Pervez, Muhammad Waqar Aslam, Anzar Mahmood, Anila Kousar, Muhammad Yamin Younis, Bilal Akbar, Ghulam Qadar Chaudhary, and Syed Kashif Hussain. "Energy Consumption Forecasting for University Sector Buildings." Energies 10, no. 10 (October 12, 2017): 1579. http://dx.doi.org/10.3390/en10101579.

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12

Javeed Nizami, SSAK, and Ahmed Z. Al-Garni. "Forecasting electric energy consumption using neural networks." Energy Policy 23, no. 12 (December 1995): 1097–104. http://dx.doi.org/10.1016/0301-4215(95)00116-6.

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13

Jozi, Aria, Tiago Pinto, Isabel Praça, and Zita Vale. "Decision Support Application for Energy Consumption Forecasting." Applied Sciences 9, no. 4 (February 18, 2019): 699. http://dx.doi.org/10.3390/app9040699.

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Energy consumption forecasting is crucial in current and future power and energy systems. With the increasing penetration of renewable energy sources, with high associated uncertainty due to the dependence on natural conditions (such as wind speed or solar intensity), the need to balance the fluctuation of generation with the flexibility from the consumer side increases considerably. In this way, significant work has been done on the development of energy consumption forecasting methods, able to deal with different forecasting circumstances, e.g., the prediction time horizon, the available data, the frequency of data, or even the quality of data measurements. The main conclusion is that different methods are more suitable for different prediction circumstances, and no method can outperform all others in all situations (no-free-lunch theorem). This paper proposes a novel application, developed in the scope of the SIMOCE project (ANI|P2020 17690), which brings together several of the most relevant forecasting methods in this domain, namely artificial neural networks, support vector machines, and several methods based on fuzzy rule-based systems, with the objective of providing decision support for energy consumption forecasting, regardless of the prediction conditions. For this, the application also includes several data management strategies that enable training of the forecasting methods depending on the available data. Results show that by this application, users are endowed with the means to automatically refine and train different forecasting methods for energy consumption prediction. These methods show different performance levels depending on the prediction conditions, hence, using the proposed approach, users always have access to the most adequate methods in each situation.

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14

Rodriguez, C. P., and G. J. Anders. "Energy Price Forecasting in the Ontario Competitive Power System Market." IEEE Transactions on Power Systems 19, no. 1 (February 2004): 366–74. http://dx.doi.org/10.1109/tpwrs.2003.821470.

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15

Klyuev, Roman V., Irbek D. Morgoev, Angelika D. Morgoeva, Oksana A. Gavrina, Nikita V. Martyushev, Egor A. Efremenkov, and Qi Mengxu. "Methods of Forecasting Electric Energy Consumption: A Literature Review." Energies 15, no. 23 (November 25, 2022): 8919. http://dx.doi.org/10.3390/en15238919.

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Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on the means and methods of planning electricity production. Forecasting is one of the planning tools since the availability of an accurate forecast is a mechanism for increasing the validity of management decisions. This study provides an overview of the methods used to predict electricity supply requirements to different objects. The methods have been reviewed analytically, taking into account the forecast classification according to the anticipation period. In this way, the methods used in operative, short-term, medium-term, and long-term forecasting have been considered. Both classical and modern forecasting methods have been identified when forecasting electric energy consumption. Classical forecasting methods are based on the theory of regression and statistical analysis (regression, autoregressive models); probabilistic forecasting methods and modern forecasting methods use classical and deep-machine-learning algorithms, rank analysis methodology, fuzzy set theory, singular spectral analysis, wavelet transformations, Gray models, etc. Due to the need to take into account the specifics of each subject area characterizing an energy facility to obtain reliable forecast results, power consumption modeling remains an urgent task despite a wide variety of other methods. The review was conducted with an assessment of the methods according to the following criteria: labor intensity, requirements for the initial data set, scope of application, accuracy of the forecasting method, the possibility of application for other forecasting horizons. The above classification of methods according to the anticipation period allows highlights the fact that when predicting power consumption for different time intervals, the same methods are often used. Therefore, it is worth emphasizing the importance of classifying the forecast over the forecasting horizon not to differentiate the methods used to predict electricity consumption for each period but to consider the specifics of each type of forecasting (operative, short-term, medium-term, long-term).

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16

Kalinchyk, V. P., M. T. Buravlova, V. V. Kalinchyk, and V. H. Skosyrev. "FORECASTING OF ENERGY CONSUMPTION, ENERGY GENERATION AND VALUE OF ENERGY RECEIVED." Scientific notes of Taurida National V.I. Vernadsky University. Series: Technical Sciences 1, no. 2 (2020): 273–49. http://dx.doi.org/10.32838/2663-5941/2020.2-1/38.

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17

Christodoulakis, Nicos M., Sarantis C. Kalyvitis, Dimitrios P. Lalas, and Stylianos Pesmajoglou. "Forecasting energy consumption and energy related CO2 emissions in Greece." Energy Economics 22, no. 4 (August 2000): 395–422. http://dx.doi.org/10.1016/s0140-9883(99)00040-7.

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18

Zuha Maksood, Fathimath, and Geetha Achuthan. "Sustainability in Oman: Energy Consumption Forecasting using R." Indian Journal of Science and Technology 10, no. 10 (February 1, 2017): 1–14. http://dx.doi.org/10.17485/ijst/2017/v10i10/97008.

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19

Ozturk, Suat, and Feride Ozturk. "Forecasting Energy Consumption of Turkey by Arima Model." Journal of Asian Scientific Research 8, no. 2 (2018): 52–60. http://dx.doi.org/10.18488/journal.2.2018.82.52.60.

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20

Dietmair, A., and A. Verl. "ENERGY CONSUMPTION FORECASTING AND OPTIMISATION FOR TOOL MACHINES." MM Science Journal 2009, no. 01 (March 18, 2009): 63–67. http://dx.doi.org/10.17973/mmsj.2009_03_20090305.

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21

Gao, Lei. "Forecasting and Analysis of Energy Consumption in China." Frontiers in Business, Economics and Management 3, no. 2 (March 16, 2022): 26–30. http://dx.doi.org/10.54097/fbem.v3i2.257.

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Energy is essential to the development of an economy and society. In recent years, China's rapid economic development has created the "China Miracle", but it has also led to a sharp increase in energy consumption in China. To ensure the achievement of the ambitious goal of reaching the carbon peak by 2030, it is of great significance to study the total energy consumption in China in order to promote the national energy conservation and emission reduction actions. This paper constructs models GM(1,1), DGM(1,1), and gray Verhulst model based on the original data of China's total energy consumption from 2001 to 2020, and constructs a combined forecasting model by the least squares method to make an economic forecast of China's energy consumption in the next five years. It provides a theoretical basis for making a reasonable energy planning.

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22

Rueda, R., M. Cuéllar, M. Molina-Solana, Y. Guo, and M. Pegalajar. "Generalised Regression Hypothesis Induction for Energy Consumption Forecasting." Energies 12, no. 6 (March 20, 2019): 1069. http://dx.doi.org/10.3390/en12061069.

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This work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.

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23

Karabulut, Korhan, Ahmet Alkan, and Ahmet Yilmaz. "Long Term Energy Consumption Forecasting Using Genetic Programming." Mathematical and Computational Applications 13, no. 2 (August 1, 2008): 71–80. http://dx.doi.org/10.3390/mca13020071.

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24

Rojas-Renteria, J. L., T. D. Espinoza-Huerta, F. S. Tovar-Pacheco, J. L. Gonzalez-Perez, and R. Lozano-Dorantes. "An Electrical Energy Consumption Monitoring and Forecasting System." Engineering, Technology & Applied Science Research 6, no. 5 (October 23, 2016): 1130–32. http://dx.doi.org/10.48084/etasr.776.

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Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecasting system is investigated. The monitoring system was installed in an actual building and the recordings were used to design and evaluate the forecasting system, based on an artificial neural network. Results show that the system can provide detailed monitoring and also an accurate forecast for a building’s consumption.

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25

Bianco, Vincenzo, Annalisa Marchitto, Federico Scarpa, and Luca A. Tagliafico. "Forecasting Energy Consumption in the EU Residential Sector." International Journal of Environmental Research and Public Health 17, no. 7 (March 27, 2020): 2259. http://dx.doi.org/10.3390/ijerph17072259.

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The present paper aims to introduce a top down methodology for the forecasting of residential energy demand in four European countries, namely Germany, Italy, Spain, and Lithuania. The methodology employed to develop the estimation is based on econometric techniques. In particular, a logarithmic dynamic linear constant relationship of the consumption is proposed. Demand is estimated as a function of a set of explaining variables, namely heating degree days and gross domestic product per capita. The results confirm that the methodology can be applied to the case of Germany, Italy, and Spain, whereas it is not suitable for Lithuania. The analysis of elasticities of the demand with respect to the gross domestic product per capita shows a negative value for Germany, −0.629, and positive values for Italy, 0.837, and Spain, 0.249. The forecasting of consumption shows that Germany and Italy are more sensitive to weather conditions with respect to Spain and an increase in the demand of 8% and 9% is expected in case of cold climatic conditions.

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26

Sözen, Adnan, M. Ali Akçayol, and Erol Arcaklioğlu. "Forecasting Net Energy Consumption Using Artificial Neural Network." Energy Sources, Part B: Economics, Planning, and Policy 1, no. 2 (July 2006): 147–55. http://dx.doi.org/10.1080/009083190881562.

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27

Meng, Ming, Dongxiao Niu, and Wei Sun. "Forecasting Monthly Electric Energy Consumption Using Feature Extraction." Energies 4, no. 10 (September 28, 2011): 1495–507. http://dx.doi.org/10.3390/en4101495.

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28

Deb, Chirag, Lee Siew Eang, Junjing Yang, and Mattheos Santamouris. "Forecasting Energy Consumption of Institutional Buildings in Singapore." Procedia Engineering 121 (2015): 1734–40. http://dx.doi.org/10.1016/j.proeng.2015.09.144.

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29

Hyeon, Jonghwan, HyeYoung Lee, Bowon Ko, and Ho-Jin Choi. "Deep learning-based household electric energy consumption forecasting." Journal of Engineering 2020, no. 13 (July 1, 2020): 639–42. http://dx.doi.org/10.1049/joe.2019.1219.

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30

Yu Shchetinin, Eugene. "Cluster-based energy consumption forecasting in smart grids." Journal of Physics: Conference Series 1205 (April 2019): 012051. http://dx.doi.org/10.1088/1742-6596/1205/1/012051.

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Zhang, Yingchen, Rui Yang, Kaiqing Zhang, Huaiguang Jiang, and Jun Jason Zhang. "Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch." IEEE Intelligent Systems 32, no. 4 (2017): 59–63. http://dx.doi.org/10.1109/mis.2017.3121551.

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32

Khan, Anam-Nawaz, Naeem Iqbal, Atif Rizwan, Rashid Ahmad, and Do-Hyeun Kim. "An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings." Energies 14, no. 11 (May 23, 2021): 3020. http://dx.doi.org/10.3390/en14113020.

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Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.

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Wei, Shangfu, and Xiaoqing Bai. "Multi-Step Short-Term Building Energy Consumption Forecasting Based on Singular Spectrum Analysis and Hybrid Neural Network." Energies 15, no. 5 (February 25, 2022): 1743. http://dx.doi.org/10.3390/en15051743.

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Short-term building energy consumption forecasting is vital for energy conservation and emission reduction. However, it is challenging to achieve accurate short-term forecasting of building energy consumption due to its nonlinear and non-stationary characteristics. This paper proposes a novel hybrid short-term building energy consumption forecasting model, SSA-CNNBiGRU, which is the integration of SSA (singular spectrum analysis), a CNN (convolutional neural network), and a BiGRU (bidirectional gated recurrent unit) neural network. In the proposed SSA-CNNBiGRU model, SSA is used to decompose trend and periodic components from the original building energy consumption data to reconstruct subsequences, the CNN is used to extract deep characteristic information from each subsequence, and the BiGRU network is used to model the dynamic features extracted by the CNN for time series forecasting. The subsequence forecasting results are superimposed to obtain the predicted building energy consumption results. Real-world electricity and natural gas consumption datasets of office buildings in the UK were studied, and the multi-step ahead forecasting was carried out under three different scenarios. The simulation results indicate that the proposed model can improve building energy consumption forecasting accuracy and stability.

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Jaržemskis, Andrius, and Ilona Jaržemskienė. "European Green Deal Implications on Country Level Energy Consumption." Folia Oeconomica Stetinensia 22, no. 2 (December 1, 2022): 97–122. http://dx.doi.org/10.2478/foli-2022-0021.

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Abstract Research background: The European Green deal set by the European Commission has launched new business models in sustainable development. Major contributions are expected in the road transport sector; as far as conventional internal combustion creates significant input in Green House Gas emission inventories. Each EU member state has an obligation to reduce GhG emission by accelerating Electric Vehicle development. In order to foster growth of EVs, there is the need of significant investment into charging infrastructures. The article propose the model of forecasting of investment based on the forecast of the growth of the amount of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger. Purpose: The aim of this article is to present a complex model for forecasting the required investments based on the fore-cast of the increase in the number of electric vehicles and their demand on energy and investments. Research methodology: The general algorithm of forecasting consists of several consecutive phases: (1) Forecasting the number of electric vehicles, (2) Forecasting the energy needed for electric vehicles, based on the forecast (1) and the predicted usage level of these vehicles. (3) Forecasting the charging station number with the expected technical capacities and characteristics of these charging stations based on the forecasts (1) and (2). (4) Forecasting the need to upgrade the low-voltage grid based on the forecast (3). (5) Calculating the total investment needed based on the results of the forecasts (3) and (4). The main limitations of the study are related to the statistics available for modelling and human behaviour uncertainty, especially in the evaluation impact of measures to foster use of electric vehicles. Results: The findings of the Lithuanian case analysis, which is expressed in three scenarios, focuses on two trends. The most promising scenario projects 319,470 electric vehicles by 2030 which will demand for 1.09 TWh of electricity, representing 8.4–9.9 percent of the total energy consumption in the country. It requires EUR 230, million in the low-voltage grid and EUR 209, million in the charging stations. Novelty: The scientific problem is that the current approach on the forecasting of electric vehicles is too abstract, forecast models cannot be transferred from country to country. This article proposes a model of forecasting investments based on the forecast of the increase in the number of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger. The article has proven that statistics-based forecasting gives very different results compared to the objective function and to the evaluation of the effects of measures. This has not been compared in previous studies.

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Shin, Sun-Youn, and Han-Gyun Woo. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms." Energies 15, no. 13 (July 2, 2022): 4880. http://dx.doi.org/10.3390/en15134880.

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In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperformed the other models in Period 2. Findings, therefore, suggested the applicability of machine learning to forecast energy consumption and also demonstrated that traditional econometric approaches may outperform machine learning when there is less unknown irregularity in the time series, but machine learning can work better with unexpected irregular time series data.

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Vale, Zita, Luis Gomes, Daniel Ramos, and Pedro Faria. "Green computing: a realistic evaluation of energy consumption for building load forecasting computation." Journal of Smart Environments and Green Computing 2, no. 2 (2022): 34–45. http://dx.doi.org/10.20517/jsegc.2022.06.

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Aim: The methodology proposed in this paper aims at analyzing the energy consumption, electricity costs, computation time, and accuracy associated with each forecasting algorithm and approach. Furthermore, a monitoring infrastructure is considered to provide inputs to the forecasting approach. Methods: The main objective is to discuss to what extent it is reasonable to increase the consumption of the forecasting approach computation and monitoring infrastructure to achieve more accurate forecasts. Artificial neural networks are used as examples to illustrate the proposed methodology in a building equipped with electricity consumption and other parameters monitoring infrastructure. Results: It has been shown that collecting many parameters and using very accurate forecasting approaches may cause an energy consumption higher than the energy consumption deviation resulting from the forecasting approach with lower accuracy. Conclusion: Finally, it has been shown that green computing, or green computation, requires considering the computation of data, the impact of collecting such data, and the need to perform highly consuming computation tasks.

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Ali, Zulfiqar, Faheemullah Shaikh, Laveet Kumar, Sadam Hussain, and Zubair Ahmed Memon. "Analysis of energy consumption and forecasting sectoral energy demand in Pakistan." International Journal of Energy Technology and Policy 17, no. 4 (2021): 366. http://dx.doi.org/10.1504/ijetp.2021.10041988.

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Ali, Zulfiqar, Faheemullah Shaikh, Laveet Kumar, Sadam Hussain, and Zubair Ahmed Memon. "Analysis of energy consumption and forecasting sectoral energy demand in Pakistan." International Journal of Energy Technology and Policy 17, no. 4 (2021): 366. http://dx.doi.org/10.1504/ijetp.2021.118338.

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Khan, Atif Maqbool, and Magdalena Osińska. "How to Predict Energy Consumption in BRICS Countries?" Energies 14, no. 10 (May 11, 2021): 2749. http://dx.doi.org/10.3390/en14102749.

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Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to predict energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the predictive ability of the FGM1,1 with traditional ones, like standard GM1,1 and ARIMA1,1,1 models. Moreover, it illustrates the view of BRICS’s nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM1,1 for a specific range of order parameters and the ARIMA1,1,1 model and the usefulness of both approaches for energy consumption efficient forecasting.

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Sujatha, R., Gowtham Sukumar, V. Rajesh, R. Sathish Kumar, and Judeson David. "Electricity Consumption Forecasting System Using Deep Learning." IOP Conference Series: Materials Science and Engineering 1258, no. 1 (October 1, 2022): 012061. http://dx.doi.org/10.1088/1757-899x/1258/1/012061.

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Energy modeling in Smart Buildings (SB) and planning and operating the generation of power based on the information extracted are key components of the Smart Grid’s (SG’s) energy management system. In the world, buildings use a significant amount of energy that contributes to energy efficiency programs. Additionally, excessive utilization power generation appliance also including air conditioners and heater, breathing, and climate control (HVAC) units, improper microclimate control, and inappropriate start-up and ordering of power equipment waste a lot of heat pumps. The utility can mitigate energy generation costs when it anticipates electrical loads and schedules generation resources in accordance with the demand. To estimate electricity usage at varying tiers of utility grid systems, a range of techniques have already been used. The goal of this study will be to create a hybrid deep learning model can predict resource utilization in infrastructures. Model building and data cleaning are the two stages of the proposed framework. Data cleaning involves pre-processing techniques and adding additional lag values to raw data. This hybrid deep learning (DL) approach is made up of a series of completely connected layered and linear Long Short-Term Memory (LSTM) sections layered over bi-directional Long Short-Term Memory (LSTM) components and is based also on collected information. You could incorporate the dependence structure of electricity usage on regressors and increase computation efficiency, training time, as well as computational complexity by using the results obtained.

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Song, Kyung-Bin, Rae-Jun Park, Kyeong-Hwan Kim, and Jong-Ryul Won. "Electrical Energy Consumption Forecasting Algorithm Using Multiple Regression Method." Journal of the Korean Institute of Illuminating and Electrical Installation Engineers 31, no. 11 (November 30, 2017): 69–74. http://dx.doi.org/10.5207/jieie.2017.31.11.069.

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Skorokhodov, V. I., O. A. Lysenko, A. V. Simakov, and S. A. Gorovoy. "Forecasting consumption of electric energy by using wavelet transform." Omsk Scientific Bulletin, no. 177 (2021): 75–78. http://dx.doi.org/10.25206/1813-8225-2021-177-75-78.

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Forecasting electricity consumption is an urgent task for generating companies, since it is currently impossible to accumulate electricity on an industrial scale. Also, the forecast is necessary for consumers to carry out technical work and other activities. The purpose of this work is to make a forecast of electric energy consumption using the wavelet transform, and to select the optimal wavelet function for forecasting. Data for forecasting is a schedule of the load of the shop, which plays the role of a household room, warehouse, as well as a working office for personnel who service electrical installations at a production enterprise. Based on the results of the work, the optimal wavelet function is selected. The result of the work is a representation of the trend of electric energy consumption by the object under consideration, i.e. a forecast presented in the form of a graph, and a detailed component of the projected consumption is obtained, which in theory is justified as interference and sharply variable nature of electricity consumption

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Anh, Le Hoang, Gwang Hyun Yu, Dang Thanh Vu, Jin Sul Kim, Jung Il Lee, Jun Churl Yoon, and Jin Young Kim. "Stride-TCN for Energy Consumption Forecasting and Its Optimization." Applied Sciences 12, no. 19 (September 20, 2022): 9422. http://dx.doi.org/10.3390/app12199422.

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Forecasting, commonly used in econometrics, meteorology, or energy consumption prediction, is the field of study that deals with time series data to predict future trends. Former studies have revealed that both traditional statistical models and recent deep learning-based approaches have achieved good performance in forecasting. In particular, temporal convolutional networks (TCNs) have proved their effectiveness in several time series benchmarks. However, presented TCN models are too heavy to deploy on resource-constrained systems, such as edge devices. As a resolution, this study proposes a stride–dilation mechanism for TCN that favors a lightweight model yet still achieves on-pair accuracy with the heavy counterparts. We also present the Chonnam National University (CNU) Electric Power Consumption dataset, the dataset of energy consumption measured at CNU by smart meters every hour. The experimental results indicate that our best model reduces the mean squared error by 32.7%, whereas the model size is only 1.6% compared to the baseline TCN.

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Bunnoon, Pituk, Kusumal Chalermyanont, and Chusak Limsakul. "Improving the Model for Energy Consumption Load Demand Forecasting." IEEJ Transactions on Power and Energy 132, no. 3 (2012): 235–43. http://dx.doi.org/10.1541/ieejpes.132.235.

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Pao, H. T. "Forecasting energy consumption in Taiwan using hybrid nonlinear models." Energy 34, no. 10 (October 2009): 1438–46. http://dx.doi.org/10.1016/j.energy.2009.04.026.

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Avami, A., and M. Boroushaki. "Energy Consumption Forecasting of Iran Using Recurrent Neural Networks." Energy Sources, Part B: Economics, Planning, and Policy 6, no. 4 (August 9, 2011): 339–47. http://dx.doi.org/10.1080/15567240802706734.

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Zhao, Huiru, and Sen Guo. "Annual Energy Consumption Forecasting Based on PSOCA-GRNN Model." Abstract and Applied Analysis 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/217630.

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Accurate energy consumption forecasting can provide reliable guidance for energy planners and policy makers, which can also recognize the economic and industrial development trends of a country. In this paper, a hybrid PSOCA-GRNN model was proposed for the annual energy consumption forecasting. The generalized regression neural network (GRNN) model was employed to forecast the annual energy consumption due to its good ability of dealing with the nonlinear problems. Meanwhile, the spread parameter of GRNN model was automatically determined by PSOCA algorithm (the combination of particle swarm optimization algorithm and cultural algorithm). Taking China’s annual energy consumption as the empirical example, the effectiveness of this proposed PSOCA-GRNN model was proved. The calculation result shows that this proposed hybrid model outperforms the single GRNN model, GRNN model optimized by PSO (PSO-GRNN), discrete grey model (DGM (1, 1)), and ordinary least squares linear regression (OLS_LR) model.

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Barak, Sasan, and S. Saeedeh Sadegh. "Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm." International Journal of Electrical Power & Energy Systems 82 (November 2016): 92–104. http://dx.doi.org/10.1016/j.ijepes.2016.03.012.

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Al-Shehri, Abdallah. "A simple forecasting model for industrial electric energy consumption." International Journal of Energy Research 24, no. 8 (2000): 719–26. http://dx.doi.org/10.1002/1099-114x(20000625)24:8<719::aid-er627>3.0.co;2-4.

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Bhatt, Dhowmya, Danalakshmi D, A. Hariharasudan, Marcin Lis, and Marlena Grabowska. "Forecasting of Energy Demands for Smart Home Applications." Energies 14, no. 4 (February 17, 2021): 1045. http://dx.doi.org/10.3390/en14041045.

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The utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal energy consumption. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a balanced deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. The study analyzes and optimizes the consumption of power in smart buildings by the HVAC systems in terms of power loss, price management and reactive power. Experiments are conducted over various scenarios to check the integrity of the system over various smart buildings and in high-rise buildings. The results are compared in terms of various HVAC devices on various metrics and communication protocols, where the proposed system is considered more effective than other methods. The results of the Li-Fi communication protocols show improved results compared to the other communication protocols.

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