- Adams, S.; Bamanga, M. Modelling and Forecasting Seasonal Behavior of Rainfall in Abuja, Nigeria; A SARIMA Approach. Am. J. Math. Stat. 2020, 10, 10–19. [CrossRef]
Paper not yet in RePEc: Add citation now
- Agyekum, E.B. Energy poverty in energy rich Ghana: A SWOT analytical approach for the development of Ghana’s renewable energy. Sustain. Energy Technol. Assess. 2020, 40, 100760. [CrossRef]
Paper not yet in RePEc: Add citation now
- Al-Shaikh, H.; Rahman, M.A.; Zubair, A. Short-Term Electric Demand Forecasting for Power Systems using Similar Months Approach based SARIMA. In Proceedings of the 2019 IEEE International Conference on Power, Electrical, and Electronics and Industrial Applications (PEEIACON), Electrical, Dhaka, Bangladesh, 29 November–1 December 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 122–126. [CrossRef]
Paper not yet in RePEc: Add citation now
- Alakeson, V.; Wilsdon, J. Digital sustainability in Europe. J. Ind. Ecol. 2002, 6, 10–12. [CrossRef]
Paper not yet in RePEc: Add citation now
- Andoh, P.Y.A.; Sekyere, C.K.K.; Mensah, L.D.; Dzebre, D.E.K. Forecasting Electricity Demand in Ghana with The Sarima Model. J. Appl. Eng. Technol. Sci. (JAETS) 2021, 3, 1–9. [CrossRef]
Paper not yet in RePEc: Add citation now
- Arghira, N.; Ploix, S.; Fǎgǎrǎşan, I.; Iliescu, S.S. Forecasting energy consumption in dwellings. Adv. Intell. Syst. Comput. 2013, 187, 251–264. [CrossRef]
Paper not yet in RePEc: Add citation now
- Arlt, J.; Trcka, P. Automatic SARIMA modeling and forecast accuracy. Commun. Stat. Simul. Comput. 2021, 50, 2949–2970. [CrossRef]
Paper not yet in RePEc: Add citation now
- ArunKumar, K.E.; Kalaga, D.V.; Kumar, C.M.S.; Chilkoor, G.; Kawaji, M.; Brenza, T.M. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: AutoRegressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Appl. Soft Comput. 2021, 103, 107161. [CrossRef]
Paper not yet in RePEc: Add citation now
- ArunKumar, K.E.; Kalaga, D.V.; Kumar, C.M.S.; Kawaji, M.; Brenza, T.M. Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends. Alex. Eng. J. 2022, 61, 7585–7603. [CrossRef]
Paper not yet in RePEc: Add citation now
Auffhammer, M.; Mansur, E.T. Measuring climatic impacts on energy consumption: A review of the empirical literature. Energy Econ. 2014, 46, 522–530. [CrossRef]
Aydin, E.; Brounen, D. The impact of policy on residential energy consumption. Energy 2019, 169, 115–129. [CrossRef]
- Babu, M.K.; Ray, P.; Sahoo, A.K. Cost and Energy Efficiency Study of an ARIMA Forecast Model Using HOMER Pro. In Recent Advances in Power Systems; Gupta, O.H., Singh, S.N., Malik, O.P., Eds.; Springer Nature: Singapore, 2023; Volume 960, pp. 137–151. [CrossRef]
Paper not yet in RePEc: Add citation now
- Barua, A.M.; Goswami, P.K. A Survey on Electric Power Consumption Prediction Techniques. Int. J. Eng. Res. Technol. 2020, 13, 2568–2575. [CrossRef]
Paper not yet in RePEc: Add citation now
- Bayindir, R.; Irmak, E.; Colak, İ.; Bektas, A. Development of a real time energy monitoring platform. Int. J. Electr. Power Energy Syst. 2011, 33, 137–146. [CrossRef]
Paper not yet in RePEc: Add citation now
- Bilgili, M.; Pinar, E. Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye. Energy 2023, 284, 128575. [CrossRef] Sustainability 2024, 16, 2958 21 of 23
Paper not yet in RePEc: Add citation now
- Bollino, C.A.; Asdrubali, F.; Polinori, P.; Bigerna, S.; Micheli, S.; Guattari, C.; Rotili, A. A note on medium- and long-term global energy prospects and scenarios. Sustainability 2017, 9, 833. [CrossRef]
Paper not yet in RePEc: Add citation now
- Bozkurt, Ö.Ö.; Biricik, G.; Tayşi, Z.C. Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market. PLoS ONE 2017, 12, e0175915. [CrossRef] [PubMed]
Paper not yet in RePEc: Add citation now
Brandoni, C.; Polonara, F. The role of municipal energy planning in the regional energy-planning process. Energy 2012, 48, 323–338. [CrossRef] Sustainability 2024, 16, 2958 23 of 23
- Brugger, H.; Eichhammer, W.; Mikova, N.; Dönitz, E. Energy Efficiency Vision 2050: How will new societal trends influence future energy demand in the European countries? Energy Policy 2021, 152, 112216. [CrossRef]
Paper not yet in RePEc: Add citation now
- Chaikumbung, M. Institutions and consumer preferences for renewable energy: A meta-regression analysis. Renew. Sustain. Energy Rev. 2021, 146, 111143. [CrossRef]
Paper not yet in RePEc: Add citation now
- Chen, C.; Hu, Y.; Karuppiah, M.; Kumar, P.M. Artificial intelligence on economic evaluation of energy efficiency and renewable energy technologies. Sustain. Energy Technol. Assess. 2021, 47, 101358. [CrossRef]
Paper not yet in RePEc: Add citation now
- Danish; Saud, S.; Baloch, M.A.; Lodhi, R.N. The nexus between energy consumption and financial development: Estimating the role of globalization in Next-11 countries. Environ. Sci. Pollut. Res. 2018, 25, 18651–18661. [CrossRef] [PubMed]
Paper not yet in RePEc: Add citation now
- De Brauwer, C.P.-S.; Cohen, J.J. Analysing the potential of citizen-financed community renewable energy to drive Europe’s low-carbon energy transition. Renew. Sustain. Energy Rev. 2020, 133, 110300. [CrossRef]
Paper not yet in RePEc: Add citation now
- Devaraj, J.; Elavarasan, R.M.; Shafiullah, G.; Jamal, T.; Khan, I. A holistic review on energy forecasting using big data and deep learning models. Int. J. Energy Res. 2021, 45, 13489–13530. [CrossRef]
Paper not yet in RePEc: Add citation now
- Dimri, T.; Ahmad, S.; Sharif, M. Time series analysis of climate variables using seasonal ARIMA approach. J. Earth Syst. Sci. 2020, 129, 149. [CrossRef]
Paper not yet in RePEc: Add citation now
- Dong, Z.; Liu, J.; Liu, B.; Li, K.; Li, X. Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification. Energy Build. 2021, 241, 110929. [CrossRef]
Paper not yet in RePEc: Add citation now
- Drame, M.; Seck, D.A.N.; Ndiaye, B.S. Analysis and Forecast of Energy Demand in Senegal with a SARIMA Model and an LSTM Neural Network. In The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023); Younas, M., Awan, I., Benbernou, S., Petcu, D., Eds.; Springer Nature: Cham, Switzerland, 2023; Volume 768, pp. 129–140. [CrossRef]
Paper not yet in RePEc: Add citation now
- Drèze, J.H.; Modigliani, F. Consumption decisions under uncertainty. J. Econ. Theory 1972, 5, 459–486. [CrossRef]
Paper not yet in RePEc: Add citation now
- Dubey, A.K.; Kumar, A.; García-Díaz, V.; Sharma, A.K.; Kanhaiya, K. Study and analysis of SARIMA and LSTM in forecasting time series data. Sustain. Energy Technol. Assess. 2021, 47, 101474. [CrossRef]
Paper not yet in RePEc: Add citation now
- Elsaraiti, M.; Ali, G.; Musbah, H.; Merabet, A.; Little, T. Time Series Analysis of Electricity Consumption Forecasting Using ARIMA Model. In Proceedings of the 2021 IEEE Green Technologies Conference (GreenTech), Denver, CO, USA, 7–9 April 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 259–262. [CrossRef]
Paper not yet in RePEc: Add citation now
Fan, Z.; Yan, Z.; Wen, S. Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health. Sustainability 2023, 15, 13493. [CrossRef]
- Fuadi, A.Z.; Haq, I.N.; Leksono, E. Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory. J. Resti (Rekayasa Sist. Dan Teknol. Inf.) 2021, 5, 466–473. [CrossRef]
Paper not yet in RePEc: Add citation now
- Gajowniczek, K.; Zabkowski, T. Electricity forecasting on the individual household level enhanced based on activity patterns. PLoS ONE 2017, 12, e0174098. [CrossRef] [PubMed]
Paper not yet in RePEc: Add citation now
- Garfield, F.R. Measuring and Forecasting Consumption. J. Am. Stat. Assoc. 1946, 41, 322–333. [CrossRef]
Paper not yet in RePEc: Add citation now
- Godil, D.I.; Sharif, A.; Rafique, S.; Jermsittiparsert, K. The asymmetric effect of tourism, financial development, and globalization on ecological footprint in Turkey. Environ. Sci. Pollut. Res. 2020, 27, 40109–40120. [CrossRef] [PubMed]
Paper not yet in RePEc: Add citation now
- Granger, C.W.J.; Pesaran, M.H. Economic and statistical measures of forecast accuracy. J. Forecast. 2000, 19, 537–560. [CrossRef]
Paper not yet in RePEc: Add citation now
- Gunnarsdottir, I.; Davidsdottir, B.; Worrell, E.; Sigurgeirsdottir, S. Sustainable energy development: History of the concept and emerging themes. Renew. Sustain. Energy Rev. 2021, 141, 110770. [CrossRef]
Paper not yet in RePEc: Add citation now
Hahn, H.; Meyer-Nieberg, S.; Pickl, S. Electric load forecasting methods: Tools for decision making. Eur. J. Oper. Res. 2009, 199, 902–907. [CrossRef]
Hankinson, G.A.; Rhys, J.M.W. Electricity consumption, electricity intensity and industrial structure. Energy Econ. 1983, 5, 146–152. [CrossRef]
- Hearne, C.J.; Makridakis, S.; Wheelwright, S.C. Forecasting Methods for Management. J. Oper. Res. Soc. 1991, 42. [CrossRef]
Paper not yet in RePEc: Add citation now
Hobbs, B.F. Optimization methods for electric utility resource planning. Eur. J. Oper. Res. 1995, 83, 1–20. [CrossRef]
- Holden, E.; Linnerud, K.; Rygg, B.J. A review of dominant sustainable energy narratives. Renew. Sustain. Energy Rev. 2021, 144, 110955. [CrossRef]
Paper not yet in RePEc: Add citation now
Huebner, G.; Shipworth, D.; Hamilton, I.; Chalabi, Z.; Oreszczyn, T. Understanding electricity consumption: A comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes. Appl. Energy 2016, 177, 692–702. [CrossRef]
- Inkulu, A.K.; Bahubalendruni, M.V.A.R.; Dara, A.; SankaranarayanaSamy, K. Challenges and opportunities in human robot collaboration context of Industry 4.0—A state of the art review. Industrial Robot 2022, 49, 226–239. [CrossRef]
Paper not yet in RePEc: Add citation now
- Johnson, O.W.; Han, J.Y.C.; Knight, A.L.; Mortensen, S.; Aung, M.T.; Boyland, M.; Resurrección, B.P. Intersectionality and energy transitions: A review of gender, social equity and low-carbon energy. Energy Res. Soc. Sci. 2020, 70, 101774. [CrossRef]
Paper not yet in RePEc: Add citation now
- Jose, R.; Panigrahi, S.K.; Patil, R.A.; Fernando, Y.; Ramakrishna, S. Artificial Intelligence-Driven Circular Economy as a Key Enabler for Sustainable Energy Management. Mater. Circ. Econ. 2020, 2, 8. [CrossRef]
Paper not yet in RePEc: Add citation now
- Kabeyi, M.J.B.; Olanrewaju, O.A. Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply. Front. Energy Res. 2022, 9. Available online: https://www.frontiersin.org/articles/10.3389/fenrg.2021.743114 (accessed on 14 February 2024). [CrossRef]
Paper not yet in RePEc: Add citation now
- Kao, Y.-S.; Nawata, K.; Huang, C.-Y. Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition. Mathematics 2020, 8, 1722. [CrossRef]
Paper not yet in RePEc: Add citation now
Kaur, A.; Nonnenmacher, L.; Pedro, H.T.C.; Coimbra, C.F.M. Benefits of solar forecasting for energy imbalance markets. Renew. Energy 2016, 86, 819–830. [CrossRef]
- Kober, T.; Schiffer, H.W.; Densing, M.; Panos, E. Global energy perspectives to 2060—WEC’s World Energy Scenarios 2019. Energy Strategy Rev. 2020, 31, 100523. [CrossRef]
Paper not yet in RePEc: Add citation now
- Lai, Y.; Dzombak, D.A. Use of the Autoregressive Integrated Moving Average (ARIMA) Model to Forecast Near-Term Regional Temperature and Precipitation. Weather. Forecast. 2020, 35, 959–976. [CrossRef]
Paper not yet in RePEc: Add citation now
- Le, T.; Vo, M.T.; Vo, B.; Hwang, E.; Rho, S.; Baik, S.W. Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM. Appl. Sci. 2019, 9, 4237. [CrossRef]
Paper not yet in RePEc: Add citation now
- Lerman, L.V.; Gerstlberger, W.; Lima, M.F.; Frank, A.G. How governments, universities, and companies contribute to renewable energy development? A municipal innovation policy perspective of the triple helix. Energy Res. Soc. Sci. 2021, 71, 101854. [CrossRef]
Paper not yet in RePEc: Add citation now
Li, D.H.W.; Yang, L.; Lam, J.C. Impact of climate change on energy use in the built environment in different climate zones—A review. Energy 2012, 42, 103–112. [CrossRef]
Li, J.; Yang, L.; Long, H. Climatic impacts on energy consumption: Intensive and extensive margins. Energy Econ. 2018, 71, 332–343. [CrossRef]
- Li, P.; Zhang, J.-S. A New Hybrid Method for China’s Energy Supply Security Forecasting Based on ARIMA and XGBoost. Energies 2018, 11, 1687. [CrossRef]
Paper not yet in RePEc: Add citation now
- Lin, B.; Su, T. Does COVID-19 open a Pandora’s box of changing the connectedness in energy commodities? Res. Int. Bus. Financ. 2021, 56, 101360. [CrossRef] [PubMed]
Paper not yet in RePEc: Add citation now
- Lin, Z.; Cheng, L.; Huang, G. Electricity Consumption Prediction Based on LSTM with Attention Mechanism. IEEJ Trans. Electr. Electron. Eng. 2020, 15, 556–562. [CrossRef]
Paper not yet in RePEc: Add citation now
- Liu, H.; Li, C.; Shao, Y.; Zhang, X.; Zhai, Z.; Wang, X.; Qi, X.; Wang, J.; Hao, Y.; Wu, Q.; et al. Forecast of the trend in incidence of acute hemorrhagic conjunctivitis in China from 2011–2019 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ETS) models. J. Infect. Public Health 2020, 13, 287–294. [CrossRef]
Paper not yet in RePEc: Add citation now
- Liu, X.; Lin, Z.; Feng, Z. Short-term offshore wind speed forecast by seasonal ARIMA—A comparison against GRU and LSTM. Energy 2021, 227, 120492. [CrossRef]
Paper not yet in RePEc: Add citation now
- Liu, Y.; Li, Z.; Yin, X. Environmental regulation, technological innovation and energy consumption—A cross-region analysis in China. J. Clean. Prod. 2018, 203, 885–897. [CrossRef]
Paper not yet in RePEc: Add citation now
- Lorente, D.B.; lvarez-Herranz, A. Economic growth and energy regulation in the environmental Kuznets curve. Environ. Sci. Pollut. Res. 2016, 23, 16478–16494. [CrossRef]
Paper not yet in RePEc: Add citation now
Lynham, J.; Nitta, K.; Saijo, T.; Tarui, N. Why does real-time information reduce energy consumption? Energy Econ. 2016, 54, 173–181. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
- Madlener, R.; Sunak, Y. Impacts of urbanization on urban structures and energy demand: What can we learn for urban energy planning and urbanization management? Sustain. Cities Soc. 2011, 1, 45–53. [CrossRef]
Paper not yet in RePEc: Add citation now
Moret, S.; Gironès, V.C.; Bierlaire, M.; Maréchal, F. Characterization of input uncertainties in strategic energy planning models. Appl. Energy 2017, 202, 597–617. [CrossRef]
- Nepal, B.; Yamaha, M.; Yokoe, A.; Yamaji, T. Electricity load forecasting using clustering and ARIMA model for energy management in buildings. Jpn. Archit. Rev. 2020, 3, 62–76. [CrossRef]
Paper not yet in RePEc: Add citation now
- Ngo, N.-T.; Pham, A.-D.; Truong, N.-S.; Truong, T.T.H.; Huynh, N.-T. Hybrid Machine Learning for Time-Series Energy Data for Enhancing Energy Efficiency in Buildings. In Computational Science—ICCS 2021; Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A., Eds.; Springer International Publishing: Cham, Switzerland, 2021; Volume 12746, pp. 273–285. [CrossRef]
Paper not yet in RePEc: Add citation now
- Nguyen, X.P.; Le, N.D.; Pham, V.V.; Huynh, T.T.; Dong, V.H.; Hoang, A.T. Mission, challenges, and prospects of renewable energy development in Vietnam. Energy Sources Part A Recovery Util. Environ. Eff. 2021, 1–13. [CrossRef]
Paper not yet in RePEc: Add citation now
- Opeyemi, B.M. Path to sustainable energy consumption: The possibility of substituting renewable energy for non-renewable energy. Energy 2021, 228, 120519. [CrossRef]
Paper not yet in RePEc: Add citation now
Ozili, P.K.; Iorember, P.T. Financial stability and sustainable development. Int. J. Financ. Econ. 2023, ijfe.2803. [CrossRef]
Pina, A.; Silva, C.; Ferrão, P. The impact of demand side management strategies in the penetration of renewable electricity. Energy 2012, 41, 128–137. [CrossRef]
- Poudel, P. Data on Energy by Our World in Data: World Energy Consumption. Available online: https://www.kaggle.com/ datasets/pralabhpoudel/world-energy-consumption (accessed on 20 December 2023).
Paper not yet in RePEc: Add citation now
- Presekal, A.; Herdiansyah, H.; Harwahyu, R.; Suwartha, N.; Sari, R.F. Evaluation of Electricity Consumption and Carbon Footprint of UI GreenMetric Participating Universities Using Regression Analysis. In E3s Web of Conferences; EDP Sciences: Ulys, France, 2018. [CrossRef]
Paper not yet in RePEc: Add citation now
- Przepiorka, W.; Horne, C. How Can Consumer Trust in Energy Utilities be Increased? The Effectiveness of Prosocial, Proenvironmental, and Service-Oriented Investments as Signals of Trustworthiness. Organ. Environ. 2020, 33, 262–284. [CrossRef]
Paper not yet in RePEc: Add citation now
- Rabbani, M.B.A.; Musarat, M.A.; Alaloul, W.S.; Rabbani, M.S.; Maqsoom, A.; Ayub, S.; Bukhari, H.; Altaf, M. A Comparison Between Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ES) Based on Time Series Model for Forecasting Road Accidents. Arab. J. Sci. Eng. 2021, 46, 11113–11138. [CrossRef]
Paper not yet in RePEc: Add citation now
- Rahman, H.; Selvarasan, I.; Begum, A.J. Short-term forecasting of total energy consumption for India-a black box based approach. Energies 2018, 11, 3442. [CrossRef]
Paper not yet in RePEc: Add citation now
Rausser, G.; Strielkowski, W.; Mentel, G. Consumer Attitudes toward Energy Reduction and Changing Energy Consumption Behaviors. Energies 2023, 16, 1478. [CrossRef]
- Ray, S.; Das, S.S.; Mishra, P.; Al Khatib, A.M.G. Time Series SARIMA Modelling and Forecasting of Monthly Rainfall and Temperature in the South Asian Countries. Earth Syst. Environ. 2021, 5, 531–546. [CrossRef]
Paper not yet in RePEc: Add citation now
- Rudnik, K.; Hnydiuk-Stefan, A.; Li, Z.; Ma, Z. Short-term modeling of carbon price based on fuel and energy determinants in EU ETS. J. Clean Prod. 2023, 417, 137970. [CrossRef]
Paper not yet in RePEc: Add citation now
Sarker, E.; Seyedmahmoudian, M.; Jamei, E.; Horan, B.; Stojcevski, A. Optimal management of home loads with renewable energy integration and demand response strategy. Energy 2020, 210, 118602. [CrossRef]
- Schaffer, A.L.; Dobbins, T.A.; Pearson, S.A. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: A guide for evaluating large-scale health interventions. BMC Med. Res. Methodol. 2021, 21, 58. [CrossRef] [PubMed]
Paper not yet in RePEc: Add citation now
- Shahbaz, M.; Topcu, B.A.; Sarıgül, S.S.; Vo, X.V. The effect of financial development on renewable energy demand: The case of developing countries. Renew Energy 2021, 178, 1370–1380. [CrossRef]
Paper not yet in RePEc: Add citation now
- Shan, S.; Cao, B.; Wu, Z. Forecasting the Short-Term Electricity Consumption of Building Using a Novel Ensemble Model. IEEE Access 2019, 7, 88093–88106. [CrossRef]
Paper not yet in RePEc: Add citation now
- Singh, J.P.; Alam, O.; Yassine, A. Influence of Geodemographic Factors on Electricity Consumption and Forecasting Models. IEEE Access 2022, 10, 70456–70466. [CrossRef]
Paper not yet in RePEc: Add citation now
Singh, S.; Parmar, K.S.; Kumar, J.; Makkhan, S.J.S. Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19. Chaos Solitons Fractals 2020, 135, 109866. [CrossRef] Sustainability 2024, 16, 2958 22 of 23
- Sloboda, B.; Montgomery, D.C.; Jennings, C.L.; Kulahci, M. Introduction to Time Series Analysis and Forecasting; Wiley: Hoboken, NJ, USA, 2008; Volume 25, p. 446. ISBN 978-0-471-65397-4.
Paper not yet in RePEc: Add citation now
Sousa, J.L.; Martins, A.G.; Jorge, H. Dealing with the paradox of energy efficiency promotion by electric utilities. Energy 2013, 57, 251–258. [CrossRef]
- Sovacool, B.K.; Griffiths, S. Culture and low-carbon energy transitions. Nat. Sustain. 2020, 3, 685–693. [CrossRef]
Paper not yet in RePEc: Add citation now
- Sovacool, B.K.; Newell, P.; Carley, S.; Fanzo, J. Equity, technological innovation and sustainable behaviour in a low-carbon future. Nat. Hum. Behav. 2022, 6, 326–337. [CrossRef]
Paper not yet in RePEc: Add citation now
- Soyler, I.; Izgi, E. Electricity Demand Forecasting of Hospital Buildings in Istanbul. Sustainability 2022, 14, 8187. [CrossRef]
Paper not yet in RePEc: Add citation now
- Srivastava, S. Causal Relationship between Electricity Consumption and GDP: Plausible Explanation on Previously Found Inconsistent Conclusions for India. Theor. Econ. Lett. 2016, 6, 276. [CrossRef]
Paper not yet in RePEc: Add citation now
- Stern, P.C. Information, incentives, and proenvironmental consumer behavior. J. Consum. Policy 1999, 22, 461–478. [CrossRef]
Paper not yet in RePEc: Add citation now
- Suganthi, L.; Samuel, A.A. Energy models for demand forecasting—A review. Renew. Sustain. Energy Rev. 2012, 16, 1223–1240. [CrossRef]
Paper not yet in RePEc: Add citation now
- Tajjour, S.; Chandel, S.S. A comprehensive review on sustainable energy management systems for optimal operation of futuregeneration of solar microgrids. Sustain. Energy Technol. Assess. 2023, 58, 103377. [CrossRef]
Paper not yet in RePEc: Add citation now
To, W.M.; Lee, P.K.C.; Lai, T.M. Modeling of Monthly Residential and Commercial Electricity Consumption Using Nonlinear Seasonal Models—The Case of Hong Kong. Energies 2017, 10, 885. [CrossRef]
Tronchin, L.; Manfren, M.; Nastasi, B. Energy efficiency, demand side management and energy storage technologies—A critical analysis of possible paths of integration in the built environment. Renew. Sustain. Energy Rev. 2018, 95, 341–353. [CrossRef]
- Xing, Y.; Li, Y.; Liu, W.; Li, W.; Meng, L.-X. Operation Energy Consumption Estimation Method of Electric Bus Based on CNN Time Series Prediction. Math. Probl. Eng. 2022, 2022, 6904387. [CrossRef]
Paper not yet in RePEc: Add citation now
- Yang, J.; Ma, T.; Ma, K.; Yang, B.; Guerrero, J.M.; Liu, Z. Trading mechanism and pricing strategy of integrated energy systems based on credit rating and Bayesian game. Energy 2021, 232, 120948. [CrossRef]
Paper not yet in RePEc: Add citation now
- Yilmaz, E.N.; Polat, H.; Oyucu, S.; Aksoz, A.; Saygin, A. Data storage in smart grid systems. In Proceedings of the 2018 6th International Istanbul Smart Grids and Cities Congress and Fair (ICSG), Istanbul, Turkey, 25–26 April 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 110–113. [CrossRef]
Paper not yet in RePEc: Add citation now
- York, R. Energy consumption trends across the globe. In Oxford Handbook of Energy and Society; Oxford University Press: Oxford, UK, 2018. [CrossRef]
Paper not yet in RePEc: Add citation now
- Zhang, W.; Lin, Z.; Liu, X. Short-term offshore wind power forecasting—A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM). Renew Energy 2022, 185, 611–628. [CrossRef]
Paper not yet in RePEc: Add citation now
- Zhang, Y.; Li, Q. A Regressive Convolution Neural Network and Support Vector Regression Model for Electricity Consumption Forecasting. In Advances in Information and Communication, Proceedings of the 2019 Future of Information and Communication Conference (FICC), San Francisco, CA, USA, 14–15 March 2019; Springer International Publishing: Berlin/Heidelberg, Germany, 2019. [CrossRef]
Paper not yet in RePEc: Add citation now
- Zhou, Y. Sustainable energy sharing districts with electrochemical battery degradation in design, planning, operation and multi-objective optimisation. Renew Energy 2023, 202, 1324–1341. [CrossRef]
Paper not yet in RePEc: Add citation now
- Zhu, G.; Sha, P.; Lao, Y.; Su, Q.; Sun, Q. Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method. Math. Probl. Eng. 2021, 2021, 6613604. [CrossRef]
Paper not yet in RePEc: Add citation now