Assessment Of Energy Potential Research Articles

Offshore wind energy potential in Shandong Sea of China revealed by ERA5 reanalysis data and remote sensing

The scale of offshore wind energy has been rapidly growing worldwide in recent years. Accurate assessment of the offshore wind energy potential and emission reduction benefits is essential to realize the ambitious targets for future installations. In this study, the Shandong Sea of China was selected as a case study, and the long-term offshore wind energy potential was assessed using ERA5 reanalysis data for the last 30 years, including the spatio-temporal variation and the technical potential of wind resources. An innovative approach to evaluate the technical potential of installed offshore wind farms based on deep learning, satellite images and ERA5 reanalysis data was proposed and applied to the Shandong Sea. The results show that (1) the highest offshore wind energy potential is observed in the northeast sea of Weihai; (2) the inter-annual variation of wind resources is relatively modest, with a pronounced monthly variability. The richest wind resources are found in the spring; (3) the electricity generation of wind resources is 12.66–30.53 GWh/year, equivalent to a reduction of 9.35–22.55 Kt of CO2 emissions; (4) as of 2023, there were 12 OWFs including 633 offshore wind turbines in Shandong Sea, generating 14,218 GWh/year of clean electricity and reducing 10,505 kt/year of CO2 emissions. The method proposed in this study is applicable to both small- and large-scale areas and allows for an accurate assessment of the electricity generation from installed offshore wind farms.

Influence of air flow features on alpine wind energy potential

Wind energy is one of the potential options to fill the gap in renewable energy production in Switzerland during the winter season when the energy demand exceeds local production capacities. With likely further rising energy consumption in the future, the winter energy deficit may further increase. However, a reliable assessment of wind energy potential in complex terrain remains challenging. To obtain such information, numerical simulations are performed using a combination of the “Consortium for Small-scale Modeling” and “Weather Research and Forecasting” (COSMO-WRF) models initialized and driven by COSMO-1E model, which allows us to simulate the influence of topography at a horizontal resolution of 300 m. Two LiDAR measurement campaigns were conducted in the regions of Lukmanier Pass and Les Diablerets, Switzerland. Observational LiDAR data and measurements from nearby wind sensor networks are used to validate the COSMO-WRF simulations. The simulations show an improved representation of wind speed and direction near the ground compared to COSMO-1E. However, with increasing height and less effect of the terrain, COSMO-WRF tends to overestimate the wind speeds, following the bias that is already present in COSMO-1E. We investigate two characteristic mountain–terrain flow features, namely waves and Foehn. The effect of mountain-induced waves of the flow is investigated through an event that occurred in the area of Diablerets. One-year analysis for the frequency of conditions that are favorable for mountain wave formation is estimated. The Foehn impact on wind was observed in the Lukmanier domain. We attempt quantification of the probability of occurrence using the Foehnix model. The result shows a high probability of Foehn occurrence during the winter and early spring seasons. Our study highlights the importance of incorporating complex terrain-related meteorological events into the wind energy assessment. Furthermore, for an accurate assessment of wind speed in complex terrain, our study suggests the necessity to have a better representation of the topography compared to COSMO-1E.

Geothermal Energy Potential Assessment and Utilization in the Absheron Oil and Gas Region

Geothermal Energy Potential Assessment and Utilization in the Absheron Oil and Gas Region

A comprehensive assessment of wind energy potential and wind farm design in a coastal industrial city

Purpose Jubail Industrial City is one of the largest industrial centers in the Middle East, offering potential opportunities for renewable energy generation. This research paper presents a comprehensive analysis of the wind resources in Jubail Industrial City and proposes the design of a smart grid-connected wind farm for this strategic location. Design/methodology/approach The study used wind data collected at three different heights above ground level – 10, 50 and 90 m – over four years from 2017 to 2020. Key parameters, such as average wind speeds (WS), predominant wind direction, Weibull shape, scale parameters and wind power density (WPD), were analyzed. The study used Windographer, an exclusive software program designed to evaluate wind resources. Findings The average WS at the respective heights were 3.07, 4.29 and 4.58 m/s. The predominant wind direction was from the north-west. The Weibull shape parameter (k) at the three heights was 1.77, 2.15 and 2.01, while the scale parameter (c) was 3.36, 4.88 and 5.33 m/s. The WPD values at different heights were 17.9, 48.8 and 59.3 W/m2, respectively. Originality/value The findings suggest that Jubail Industrial City possesses favorable wind resources for wind energy generation. The proposed smart grid-connected wind farm design demonstrates the feasibility of harnessing wind power in the region, contributing to sustainable energy production and economic benefits.

Outlook for Offshore Wind Energy Development in Mexico from WRF Simulations and CMIP6 Projections

This study presents a comprehensive assessment of the offshore wind energy potential in Mexico across 40 years (1979–2018) of numerical simulations using the Weather Research and Forecasting (WRF) model and data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The WRF identifies three regions with moderate to good wind potential: off the north coast of Tamaulipas (Zone I), the northwest coast of Yucatan (Zone II), and the Gulf of Tehuantepec (Zone III). The analysis involves comparing 47 CMIP6 climate models with the WRF results and selecting the best performing models to obtain future projections for the short term (2040–2069) and the long term (2070–2099). Two ensemble-based strategies were implemented. The first one, which uses an intersection approach from which four CMIP6 models were considered, reveals positive percentage differences in Zone II for both future projections, especially for the long-term one. In Zones I and III, positive values are also observed near the coast, mainly for the long-term projection, but they are considerably lower compared to those in Zone II. The second ensemble strategy uses weight assignment through the Mean Absolute Percentage Error, so that a greater weight is given to the model that performed better in each particular zone, potentially providing more accurate results. The findings suggest the likelihood of increased offshore wind energy in these three zones of Mexico, for both short- and long-term future projections, with positive percentage differences of up to 10% in certain areas.

Using remote sensing technique to assess biomass energy potentials and appropriate site locations in Nigeria

In a bid to promote clean energy usage and conserve the forests, the Nigerian bio-energy policy was recently constituted, to help tackle the energy insecurity in the country and help increase its carbon credit in line with the United Nations development goals. This paper presents a Geographical Information System (GIS)-based assessment of biomass energy potentials and appropriate siting of biomass plant in Nigeria using remote sensing technique. It promotes sustainable and clean energy penetration and conserve forest life, which supports the UN SDGs on; Clean and Affordable Energy (SDG 7), Climate Action (SDG 13) and Life on Land, including forest sustainability (SDG 15), and the Federal Government of Nigeria's net-zero emission commitment. The weighted overlay decision analysis was employed considering multicriteria approach. The various vegetation, forest, water bodies, lands, water source locations, accessibility to roads, aspect and topography were the criteria that were considered for locating the biomass facility. Efforts were made to quantify the biomass energy potentials of the six geopolitical zones in Nigeria, with the possibility of locating biomass plant; North East, North West, North Central, South East, South West and South South. The results obtained in this study reflect that the north eastern part of Nigeria has the highest theoretical, technical and economical energy potentials of crop residues (1163.32, 399.73 and 110.56 PJ/yr, respectively), while the south eastern part has the least energy potential (52.36, 17.99 and 4.98 PJ/yr, respectively). Also, the north western part of Nigeria has the highest theoretical, technical and economical energy potentials of forest residues (260.18, 156.11 and 43.18 PJ/yr, respectively), while the south eastern part has the least energy potential (1.79, 1.08 and 0.30 PJ/yr, respectively). The remote sensing technique employed in this study suggested that the northern part of Nigeria is most suitable for siting biomass plants in the country.

Comprehensive onshore wind energy assessment in Malawi based on the WRF downscaling with ERA5 reanalysis data, optimal site selection, and energy production

Malawi, despite its minimal CO2 emissions, faces huge climate impacts and a low electrification rate. Recognizing these challenges, this study presents a comprehensive and nationwide assessment of Malawi's wind energy potential, leveraging the high-resolution capabilities of the Weather Research and Forecasting (WRF) model. Unlike most site-specific studies, this study covers the entire country, factoring in different turbine hub heights. Through sensitivity analysis, the optimal configuration for the WRF model in Malawi was determined and ERA5 reanalysis data was used for initial and boundary conditions. The study's findings were validated using MERRA-2 reanalysis and actual observational datasets. A combination of GIS and the fuzzy analytic hierarchy process (AHP) was employed, integrating key factors like land use and topographical complexity to determine optimal wind farm sites. Notably, the wind suitability map uncovered significant potential for wind farm development. Furthermore, in the evaluation of a 50 MW wind farm, 80 m hub heights consistently outperformed 100 m elevations, both technically and economically. An 80 m wind farm at Site A can potentially power up to 32,931 urban and 84,475 rural households. Economically, 80 m hub heights showcased lower levelized cost of electricity (LCOE) values and faster payback periods than 100 m ones. Furthermore, these proposed wind projects can markedly reduce CO2 emissions, positioning Malawi in tandem with global sustainability aspirations. Overall, this study provides a key foundation for Malawi's renewable energy strategies, highlighting the potential for improved electrification and better climate change mitigation outcomes.

Probabilistic assessment of wind power plant energy potential through a copula-deep learning approach in decision trees

In the face of environmental degradation and diminished energy resources, there is an urgent need for clean, affordable, and sustainable energy solutions, which highlights the importance of wind energy. In the global transition to renewable energy sources, wind power has emerged as a key player that is in line with the Paris Agreement, the Net Zero Target by 2050, and the UN 2030 Goals, especially SDG-7. It is critical to consider the variable and intermittent nature of wind to efficiently harness wind energy and evaluate its potential. Nonetheless, since wind energy is inherently variable and intermittent, a comprehensive assessment of a prospective site's wind power generation potential is required. This analysis is crucial for stakeholders and policymakers to make well-informed decisions because it helps them assess financial risks and choose the best locations for wind power plant installations. In this study, we introduce a framework based on Copula-Deep Learning within the context of decision trees. The main objective is to enhance the assessment of the wind power potential of a site by exploiting the intricate and non-linear dependencies among meteorological variables through the fusion of copulas and deep learning techniques. An empirical study was carried out using wind power plant data from Turkey. This dataset includes hourly power output measurements as well as comprehensive meteorological data for 2021. The results show that acknowledging and addressing the non-independence of variables through innovative frameworks like the Copula-LSTM based decision tree approach can significantly improve the accuracy and reliability of wind power plant potential assessment and analysis in other real-world data scenarios. The implications of this research extend beyond wind energy to inform decision-making processes critical for a sustainable energy future.

Multicriteria GIS-based assessment of biomass energy potentials in Nigeria.

The understanding of the geographical variability of biomass energy is an essential requirement for the optimal location of biomass energy conversion plants. This research presents a multicriteria GIS-based assessment of biomass energy potentials and the appropriate siting of biomass plants in Nigeria. The study applies the weighted overlay multicriteria decision analysis method. Crop and forest areas, settlement (energy supply areas), shrub/grasslands, barren land, water bodies, distance from water sources, road accessibility, topography, and aspect are the criteria that were considered for locating a biomass facility in this study. The results suggest that the theoretical, technical, and economical energy potentials of crop residues are highest in the North-East region of Nigeria and estimated at 1,163.32, 399.73, and 110.56PJ/yr, respectively, and lowest in the South-East at 52.36, 17.99, and 4.98PJ/yr, respectively. The theoretical, technical, and economical energy potentials of forest residues are highest in the North-West, estimated at 260.18, 156.11, and 43.18PJ/yr, respectively, and lowest in the South-East at 1.79, 1.08, and 0.30PJ/yr, respectively. Although most areas were identified to be suitable for siting biomass plants across Nigeria, the most suitable areas are located in the northern part of the country and include Niger, Zamfara, the Federal Capital Territory, Nassarawa, Kano, Kebbi, Kaduna, and Borno State. The study supports the Nigerian bio-energy policy that proposes to effectively utilize Nigeria's non-fuelwood as a substitute for the felling of trees. This is very important to strengthen its commitment at the COP26 International Climate Conference, which is to conserve and restore its forest. Furthermore, this study will serve as a good reference for policymakers to make well-informed decisions on tackling the energy insecurity in Nigeria.

Behavior of Amagmatic Orogenic Geothermal Systems: Insights From the Agua Blanca Fault, Baja California, Mexico

AbstractAmagmatic geothermal systems within regional‐scale orogenic faults are promising renewable resources for heat and possibly electricity production. However, their behavior needs to be better understood to improve their exploration and assessment of energy potential. To provide more insight, we report geochemical, geological, and geophysical studies from seven hot spring sites strung along a 90 km segment of the Agua Blanca Fault, which traverses a mountainous region of northern Baja California, Mexico. Our results show that topographic heads drive infiltration of meteoric water deep into basement rocks, where it is heated according to the local geothermal gradients. Long paths lead to long water residence times and high 3He/Hetotal fractions. The hot water ascends along preferentially permeable zones within the ABF, discharging at temperatures from 37°C in inland springs to 102°C on the Pacific coast. Higher discharge temperatures correlate positively with the degree of extensional fault displacement (a proxy for fault permeability). Correlations between hydraulic head gradients, residence times, and 3He/Hetotal of the thermal waters show that the hydraulic head gradient controls the length and depth of the flow paths, whereas the magnitudes and locations of the discharge sites are controlled by fault permeability. Optimal conditions at the coast allow the 120°C temperature threshold for electricity production to be reached at relatively shallow depths (<2 km), demonstrating the potential of orogenic geothermal systems not only for exploitation of hot discharging water but also for EGS exploitation of the hot rocks that surround the water upflow zones.

Assessment of wind and wave energy potential along the Indian coast

Assessment of wind and wave energy potential along the Indian coast

Integration of Land Use Potential in Energy System Optimization Models at Regional Scale: The Pantelleria Island Case Study

In the context of the energy transition, the integration of land use considerations into energy planning can provide significant improvements. In energy system optimization models (ESOMs), land use aspects can be integrated at the cost of a finer spatial resolution and a more detailed characterization of land, tailored to regional constraints and specificities. Additionally, an assessment of trade-offs with alternative land uses is necessary. Nevertheless, they are commonly neglected. This study addresses the challenge of incorporating land use aspects into ESOMs, with a focus on the unique context of Pantelleria Island. It aims to bridge the gap in methodologies for renewable energy potential assessment and model integration, considering the critical role of land pricing and availability. It combines geospatial data aggregation with model adaptation to include detailed land use aspects. The findings highlight the substantial impact of land costs on renewable energy planning, with land pricing significantly altering model outcomes. This research offers key insights for sustainable energy planning and underscores the importance of considering land use in energy transition strategies.

Climate, water, hydropower, wind speed and wind energy potential resources assessments using weather time series data, downscaled regional circulation models: A case study for Mono River Basin in the Gulf of Guinea region

Hydropower and wind energy are important renewable energies source in Gulf Guinea region, but they are sensitive to climate change, because the changing climate may alter atmospheric and hydrological conditions. In this study, firstly we analyzed the historical hydro-climate data in connection with changes in Mono river of Gulf Guinea region discharge and their associated impacts on hydropower generation and assessed the future hydropower availability in different climate scenarios. Secondly, statistical methods are used to examine past and future wind speed (WS) and wind energy potential. We used historical data (1981–2010) obtained from the National Meteorology Agency in Togo and Benin and a multi-model ensemble for future projections data (2021–2050) of eight selected Regional Climate Models (RCMs) under Representative Concentration Pathway (RCP) under scenarios 4.5 and 8.5. We found in the recent past years that the temperature and evaporation increase during dry season, while flows and hydropower generation decreased. The multi-model ensemble used have announced that in general, the decreasing trend of the rainfall and increasing trend of temperature is projected to be higher in RCP 8.5 than RCP 4.5, in the near future (2021–2050). Overall, the annual hydropower potential the hydropower will decrease between 8 and 19.3% under the RCP4.5 and 9 and 29% under the RCP8.5 while the monthly hydropower potential is projected to decrease by 4–41% under the RCP4.5 and by 6–47% under the RCP8.5 in the same period. Concerning the wind speed and the potential wind power, the observed results show that both the Weibull and Rayleigh approximations provided a better fit for the wind speed data. The projected of wind speed and wind power density (2021–2050) at an annual timescale is similar to the monthly timescale of wind speed and wind power density at 90 m height. The multi-model ensemble indicate that projected wind speeds (WS) are between 5 and 10 m/s under the RCP4.5 and, between 3 and 12 m/s under the RCP8.5 while wind power density is projected by 15–250 w/m2, and by 13–357 w/m2 under the RCP4.5 and under the RCP8.5, respectively. South and North of Mono basin, have higher WPD and are promising for installing wind turbines for small-scale power generation.

Energy potential assessment and geospatial site suitability analysis for crop residue-based power plants in Pakistan

Biomass is one of the most investigated sustainable alternative energy resources. Agricultural crop residue is abundantly available, spatially scattered and low energy-density biomass thus location of residue-based power plants is important in bioenergy development. In this study, Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) were employed to identify the best locations for crop residue-based power plants using crop production data and other geospatial datasets. Residue availability and Electricity Generation Potential (EGP) of major crops of Pakistan were computed and mapped on district level. This energy intensity (kWh/ha) map and other factors including road networks, Land Use Land Cover, grid stations and water stress were then weighed using the AHP for site suitability of power plants. Results showed that a total of 21,390 GWh of electricity could be generated. Almost 413,970 km2 (52 % of total) is unavailable for analysis because of the unavailability of crop residue, steep slope and presence of surface water. Weighted overlay analysis, based on the AHP, resulted in highly and extremely suitable locations in central Punjab and upper Sindh provinces with some areas in north-eastern Balochistan. A total of 10 final sites for residue-based power plants were identified in Pakistan, of capacity ranging from 50 MW to 125 MW, with cumulative capacity of 930 MW and 2.9 MtCO2 GHG emission reduction in a year. It can be concluded that Punjab and Sindh have the most suitable areas for development of biomass power plants. Policy making and implementation for establishment of crop residue-based power generation by stake holders can transform the region socio-economically.

Energy potential assessment and techno–economic analysis of micro hydro–photovoltaic hybrid system in Goda Warke village, Ethiopia

Abstract Rural Ethiopia has significant untapped potential for hydro and solar energy generation systems. However, challenges arise from seasonal variations and unfavourable topographic positions of flowing rivers, hindering the efficient exploitation of these resources. Despite the country’s abundance in hydro and solar energy resources, >75% of the population still lack access to electricity from the national grid. This work deals with energy resource potential assessment and techno–economic analysis of micro hydro–photovoltaic (PV) hybrid systems, considered in the case study of Goda Warke village, located in the Yaya Gulele district. A novel framework is proposed that utilizes the Natural Resource Soil Conservation Service curve number method to assess the energy potential of micro-hydro energy in ungauged basins, specifically at the exit point of the Girar River basin catchment. The average monthly flow rate in the basin is 0.975 m3/s, while the area exhibits a solar radiation potential of 5.39 kWh/m2/day. Energy policy promotes expanding access to modern energy sources and utilization of indigenous energy resources. Simulation results indicate that the hydro/PV/diesel generator (DG)/battery and hydro/PV/battery systems are the most optimal choices based on net present cost, with the inclusion of a DG for economic comparison. Micro-hydro energy covers most of the electric load in the area, achieving a capacity factor of 47.5%. The cost of energy and net present cost were found to be sensitive to variables such as the price of diesel fuel, pipe head loss, and the growth of the village load. The optimized system demonstrated a hydro energy potential of 1405.37 MWh/year and a PV energy output of 274.04 MWh/year, resulting in a levelized cost of energy of 0.0057 and 0.049 $/kWh for the hydro and PV components, respectively.

Assessment of wind energy potential in Zambia

An assessment of potential for wind energy in Zambia was carried out to help address the shortage of energy due to increasing energy needs arising from energy requirements for newly opened mining industries and unreliable hydropower generation due to climate change. The assessment was carried out by collecting wind speed data of 25 sites owned by Zambia Meteorological Department. The objective of the study was to analyse wind patterns and determine areas in Zambia where production of electricity from wind can be invested. The analysis was based on the use of Weibull shape factor and yielded 3.32 m/s wind speed and 13.2 shape factor from a site with the most suitable wind speeds (Kasama Meteorological Station). A correlation study was carried out using these results and Goldwin 1.5 MW wind turbine of model GW82/1500. The study results showed that 44.46 kW power output could be obtained at 3.83 m/s wind speed by a correlation equation of this turbine power output with wind speeds. A series of similar power output results were obtained for wind speeds recorded in the years from 2013 to 2021 and compared with those obtained in the same period by using model equation due to Al Buhariri, and found that the power outputs compared very closely at low wind speeds ranging 3 to 3.2 m/s but rather widely at higher wind speeds.This study is significant in that it provides information on decision making and helps determine policy direction for wind utilization in Zambia and it can also help influence investment decision by Zambian mining companies who have financial ability to undertake large scale investments.

Geographical information system based assessment of various renewable energy potentials in Nigeria

Nigeria’s energy transition plan is hinged on increasing the energy mix from renewable energy sources. An understanding of where the potentials of these renewable energy sources lie is very crucial to enhance quality and timely policy formulations, interventions and proper decision making. This paper aims at providing a holistic assessment of the potentials of key renewable energy resources; solar, wind, biomass and small hydro in Nigeria using ArcGIS; a geospatial analytical platform that provide data and visualizations of these potentials across the country. This paper focuses on the Inverse Distance weighting methodology which overlays various map shapefiles and spreadsheets containing relevant data such as; waterlines and water areas, road and rail networks, crop production, longitude and latitude, wind speeds, solar irradiation, elevation and other relevant map data, shape files for all the Nigerian states and local governments. These data are geo-processed to align the results to the exact locations as provided by their geographical positions stem coordinates. These interpolation results are further reclassed within standard limits for the various output parameters such as crops, forest areas, built-up areas, water bodies, shrubs/grasslands, barren lands and waterbodies to reveal suitability areas for siting the various renewable energy power generating plants.

Wind energy potential assessment: a case study in Central India

Access to sustainable energy is important for economic development and environmental protection. The shift towards renewable energy is essential for mitigating climate change and ensuring long-term energy sustainability. Though developing nations possess significant renewable energy capacity, transitioning to renewable sources remains a challenge. India's expanding wind energy sector has the potential to lower carbon dioxide emissions and produce energy in a sustainable manner. This research uses the Windographer software to conduct a comprehensive analysis of the wind energy potential at a distinct site in Nagpur, India. The assessment includes monthly average wind speeds, wind rose diagrams that show the prevailing wind directions, and wind power density (WPD) calculations. These factors together establish the site's classification. In this study, Weibull parameters are derived using four distinct methods to estimate the probability distribution function. The average WPD is found to be 13.63 and 71.08 W/m2 at heights of 10 and 100 m above ground level, respectively. Through wind turbine output analysis, numerous turbine models are assessed for suitability at the specified location. The Windtec DD 3000-125 model, interestingly, has a maximum capacity factor of 9.27%. The results highlight comparatively low wind potential in the studied region. Nevertheless, a wind turbine at this location can produce about 2 412 379 kW of electricity annually using the aforementioned wind turbine model.

THE COMPARISON SOME EXTENDED WEIBULL DISTRIBUTION (WEIBULL (W), EXPONENTIATED WEIBULL (EW), EXPONENTIATED EXPONENTIAL WEIBULL (EEW) AND ADDITIVE WEIBULL (AW)) FOR THE WIND SPEED PROBABILITY MODELLING

Accurate wind speed modeling is critical in estimating wind energy potential for harnessing wind power effectively. One of the bases for assessment of wind energy potential for a specified region is the probability distribution of wind speed, therefore wind speed data is needed to produce statistical modeling, especially in determining the best probability distribution. For this purpose, several modified weibull distributions will be used and tested to determine the best model to describe wind speed in Pekanbaru. The main goal of this study is to find the best fitting distribution to the daily wind speed measured over Pekanbaru region for the years 1999-2020 by using the four modified weibull distributions, namely Weibull (W), Exponentiated Weibull (EW), Additive Weibull (AW) and Exponentiated Exponential Weibull (EEW). The maximum likelihood method will be used to get the estimated parameter value from the distribution used in this study. Furthermore, the graphical inspection (density-density plot and cumulative plot) and numerical criteria (Akaike’s information criterion (AIC), Bayesian Information Criteria (BIC), - log likelihood (- l) ) were used to determine the best fit model. In most cases, the results produced by the graphical inspection were similar, and differed from the numerical criteria . The best fit result was chosen as the distribution with the lowest values of AIC, BIC and - l. In general, the Exponentiated Exponential Weibull (EEW) distribution has been selected as the best model.

Application of machine learning technique to predict the energy performance of energy tunnels

This study investigates the application of a machine learning technique in the field of energy tunnels (i.e. tunnels equipped as ground heat exchangers), as a method to efficiently predict and optimise the thermal exchange during the operational phase and reduce the related computational time and resources. Artificial Neural Networks (ANN) models were trained based on short-term (i.e. some years) numerical simulation data. Then, the accuracy of the trained model was assessed by comparing its results for longer operational periods under the same or different thermal operation patterns with respect to those used for training. Numerical simulations were run for long-term periods, and the obtained numerical results were assumed to be the target to verify the accuracy of the prediction. The analyses under different scenarios indicate that the trained models are able to accurately and rapidly predict the thermal exchange of energy tunnels, not only for future years under the same operational pattern, but also for other thermal operation patterns. In this study, using the same computational machine, a 20-year numerical forecast of thermal output takes hours or days depending on complexity of the numerical model, but it can be reduced to minutes by using the proposed ANN model. This represents a first attempt demonstrating that with further development, machine learning techniques could be used in the practice, in combination with short-term monitoring data, as a tool for the assessment of long-term energy potential and optimisation of energy tunnels during operation.