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In relation to the situation caused by the pandemic, which may also take place in the future, there is a need to find effective solutions to improve the economic situation of the floristry industry. The production and sale of flowers is time-consuming and long-term. Therefore, any information that causes the impossibility of selling the plants will result in a reduction of profitability or bankruptcy of such companies. Research on rationally utilizing biowaste from plant cultivation as well as unsold flowers for environmental protection and effective use of their potential as a raw material for bioenergy production were examined in this article. The aim of this study was to analyze the energetic potential of the biodegradable fraction of waste from floriculture. The trials included floricultural waste containing the stems, leaves and flowers of different species and hybrid tulips (Tulipa L.), roses (Rosa L.), sunflowers (Helianthus L.) and chrysanthemums (Dendranthema Des Moul.). Their biogas and methane production as well as heat of combustion were determined experimentally. The calorific value was calculated on the basis of results from selected floricultural waste and its chemical composition. The biogas production was tested on different levels of plant material fragmentation (chaff, macerate) in fermentation processes with two ranges of temperature (meso- and thermophilic fermentation). The presented results show that the highest calorific values were determined for dry stems of roses (18,520 kJ/kg) and sunflowers (18,030 kJ/kg). In turn, the lowest were obtained for dried chrysanthemums and tulips, for which the heating value reached 15,560 kJ/kg and 15,210 kJ/kg. In addition, based on one ton of the fresh mass of biowaste from floriculture, the largest biogas production including the control was obtained from the chrysanthemum chaff by mesophilic anaerobic digestion. Moreover, the largest volume of methane was received by thermophilic anaerobic digestion of roses. The highest content of biomethane (56.68%) was reached by thermophilic fermentation of roses. The energy production of the analyzed substrates was also calculated, based on the amount of biogas produced in the containers for anaerobic digestion. Additionally, a deep neural network model, which predicted the production of methane gas, was created. Owing to the properties of the network, the level of significance of variables used for modelling and prediction of biogas production was determined. The neural modelling process was carried out with the use of the H2O program.

Advances in metering technologies and emerging energy forecast strategies provide opportunities and challenges for predicting both short and long-term building energy usage. Machine learning is an important energy prediction technique, and is significantly gaining research attention. The use of different machine learning techniques based on a rolling-horizon framework can help to reduce the prediction error over time. Due to the significant increases in error beyond short-term energy forecasts, most reported energy forecasts based on statistical and machine learning techniques are within the range of one week. The aim of this study was to investigate how facility managers can improve the accuracy of their building’s long-term energy forecasts. This paper presents an extensive study of machine learning and data processing techniques and how they can more accurately predict within different forecast ranges. The Clarendon building of Teesside University was selected as a case study to demonstrate the prediction of overall energy usage with different machine learning techniques such as polynomial regression (PR), support vector regression (SVR) and artificial neural networks (ANNs). This study further examined how preprocessing training data for prediction models can impact the overall accuracy, such as via segmenting the training data by building modes (active and dormant), or by days of the week (weekdays and weekends). The results presented in this paper illustrate a significant reduction in the mean absolute percentage error (MAPE) for segmented building (weekday and weekend) energy usage prediction when compared to unsegmented monthly predictions. A reduction in MAPE of 5.27%, 11.45%, and 12.03% was achieved with PR, SVR and ANN, respectively.

Heating, ventilating and air-conditioning (HVAC) systems are typical non-linear time-variable multivariate systems with disturbances and uncertainties. In this paper, an approach based on a combined neuro-fuzzy model for dynamic and automatic regulation of indoor temperature is proposed. The proposed artificial neural network performs indoor temperatures forecasts that are used to feed a fuzzy logic control unit in order to manage the on/off switching of the HVAC system and the regulation of the inlet air speed. Moreover, the used neural network is optimized by the analytical calculation of the embedding parameters, and the goodness of this approach is tested through MATLAB. The fuzzy controller is driven by the indoor temperature forecasted by the neural network module and is able to adjust the membership functions dynamically, since thermal comfort is a very subjective factor and may vary even in the same subject. The paper shows some experimental results, through a real implementation in an embedded prototyping board, of the proposed approach in terms of the evolution of the inlet air speed injected by the fan coils, the indoor air temperature forecasted by the neural network model and the adjusting of the membership functions after receiving user feedback.

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values.

The synchronous generators (SGs) supplying non-linear loads have harmonically distorted terminal voltages. Hence, these distorted terminal voltages adversely affect the performance parameters of the supplied loads such as the power factor, current distortion, losses, and efficiency. To mitigate the harmonic voltages and currents, passive and active filters are generally employed. However, passive filters cause resonance problems, while active filters can cause high costs. On the other hand, in several recent studies to reduce the SG’s terminal voltage harmonic distortion, which depends on the constructional design under the no-loading condition, the conventional DC excitation current has been modulated with AC harmonic components. These field current modulation methods have high computational complexity, and require extra hardware for their implementation. In the present paper, firstly, for the reduction of the terminal voltage harmonic distortion of the SG under non-linear loading conditions, the validity of the field current modulation technique is investigated. The numerical results show that by using the field current modulation method, under rated loading conditions, the total harmonic distortion of the terminal voltage can be reduced from 18% to 11%. Secondly, to provide a computational efficient and low-cost tool for optimal field current modulation, which minimizes the terminal voltage harmonic distortion, an Artificial Neural Network (ANN)-based model is proposed. Finally, with the integration of ANSYS Maxwell, ANSYS Simplorer, and MATLAB/Simulink software, the implementation of the developed model is demonstrated for the construction of the optimally modulated field current.

The major challenge facing renewable energy systems in Nigeria is the lack of appropriate, affordable, and available meteorological stations that can accurately provide present and future trends in weather data and solar PV performance. It is crucial to find a solution to this because information on present and future solar PV performance is important to renewable energy investors so that they can assess the potential of renewable energy systems in various locations across the country. Although Nigerian weather provides favorable weather conditions for clean power generation, there is little penetration of renewable energy systems in the region, since over 95% of the power is fossil-fuel-generated. This is because there has been no detailed report showing the potential of clean power generation systems due to the dysfunctional meteorological stations in the country. This paper sought to fill this knowledge gap by providing a machine-learning-inspired forecasting of environmental weather parameters that can be used by manufacturing companies in evaluating the profitability of siting renewable energy systems in the region. Crucial weather parameters such as daily air temperature, relative humidity, atmospheric pressure, wind speed, and rainfall were obtained from NASA for a period of 19 years (viz. 2004–2022), resulting in the collection of 6664 high-resolution data points. These data were used to build diverse regressive neural networks with varying hyperparameters to find the best network arrangement. In summary, a low mean-squared error of 7 × 10−3 and high regression correlations of 96% were obtained during the training.

The transformer is one of the most important electrical machines in electrical systems. Its proper operation is fundamental for the distribution and transmission of electrical energy. During its service life, it is under continuous electrical and mechanical stresses that can produce diverse types of damage. Among them, short-circuited turns (SCTs) in the windings are one of the main causes of the transformer fault; therefore, their detection in an early stage can help to increase the transformer life and reduce the maintenance costs. In this regard, this paper proposes a signal processing-based methodology to detect early SCTs (i.e., damage of low severity) through the analysis of vibroacoustic signals in steady state under different load conditions, i.e., no load, linear load, nonlinear load, and both linear and nonlinear loads, where the transformer is adapted to emulate different conditions, i.e., healthy (0 SCTs) and with damage of low severity (1 and 2 SCTs). In the signal processing stage, the contrast index is analyzed as a fault indicator, where the Unser and Tamura definitions are tested. For the automatic classification of the obtained indices, an artificial neural network is used. It showed better results than the ones provided by a support vector machine. Results demonstrate that the contrast estimation is suitable as a fault indicator for all the load conditions since 89.78% of accuracy is obtained if the Unser definition is used.

The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neural network or long short-term memory network. Historical data related to energy consumption are used to construct a nested fuzzy cognitive map in order to better understand energy use behavior. Through the experiments, the usefulness of the nested structure in energy demand prediction is demonstrated, by calculating three popular metrics: Mean Square Error, Mean Absolute Error and the correlation coefficient. A comparative analysis is performed, applying classic multilayer perceptron artificial neural networks, long short-term memory networks and fuzzy cognitive maps. The results confirmed that the proposed approach outperforms the classic methods in terms of prediction accuracy. Moreover, the advantage of the proposed approach is the ability to present complex time series in the form of a clear nested structure presenting the main concepts influencing energy consumption on the first level. The second level allows for more detailed problem analysis and lower forecast errors.

Since accurate load forecasting plays an important role in the improvisation of buildings and as described in EU’s “Green Deal”, financial resources saved through improvisation of the efficiency of buildings with social importance such as hospitals, will be the funds to support their mission, the social impact of load forecasting is significant. In the present paper, eight different machine learning predictors will be examined for the short-term load forecasting of a hospital’s facility building. The challenge is to qualify the most suitable predictors for the abovementioned task, which is beneficial for an in-depth study on accurate predictors’ applications in Intelligent Energy Management Systems (IEMS). Three Artificial Neural Networks using a backpropagation algorithm, three Artificial Neural Networks using metaheuristic optimization algorithms for training, an Adaptive Neuro-Fuzzy Inference System (ANFIS), and a Long-Short Term Memory (LSTM) network were tested using timeseries generated from a simulated healthcare facility. ANFIS and backpropagation-based trained models outperformed all other models since they both deal well with complex nonlinear problems. LSTM also performed adequately. The models trained with metaheuristic algorithms demonstrated poor performance.

The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to ensure maximal use of current circuits, system flexibility and efficiency, as well as the relatively easy integration of renewable energy resources at all voltage levels. The current revolution in communication technologies and the Internet of Things (IoT) offers us an opportunity to supervise and regulate the power grid, in order to achieve more reliable, efficient, and cost-effective services. One of the most critical aspects of efficient power system operation is the ability to predict energy load requirements, i.e., load forecasting. Load forecasting is essential for balancing demand and supply and for determining electricity prices. Typically, load forecasting has been supported through the use of Artificial Neural Networks (ANNs), which, once trained on a set of data, can predict future loads. The accuracy of the ANNs’ prediction depends on the quality and availability of the training data. In this paper, we propose novel data pre-processing strategies, which we apply to the data used to train an ANN, and subsequently evaluate the quality of the predictions it produces, to demonstrate the benefits gained. The proposed strategies and the obtained results are illustrated using consumption data from the Greek interconnected power system.

As the amount of information collected by wind turbines continues to grow, so too does the potential of its leveraging. The application of machine learning techniques as an advanced analytic tool has proven effective in solving tasks whose inherent complexity can outreach expert-based ability. Such is the case presented by this study, in which the dataset to be leveraged is high-dimensional (79 turbines × 7 SCADA channels) and high-frequency (1 Hz). In this paper, a series of machine learning techniques is applied to the retrospective power performance analysis of a withheld test set containing SCADA data collectively representing 2 full days worth of operation at the Horns Rev I offshore wind farm. A sequential machine-learning based methodology is thoroughly explored, refined, then applied to the power performance analysis task of identifying instances of abnormal behaviour; namely instances of wind turbine under and over-performance. The results of the final analysis suggest that a normal behaviour model (NBM), consisting of a uniquely constructed artificial neural network (ANN) variant trained on abnormality filtered dataset, indeed proves effective in accomplishing the power performance analysis objective. Instances of over and under performance captured by the developed NBM network are presented and discussed, including the operation status of the turbines and the uncertainty embedded in the prediction results.

© 2020 by the authors., Global warming represents a serious challenge, which requires the adoption of renewable energy technologies worldwide. However, it can negatively affect the availability of renewable energy resources, such as wind, which are needed for electricity generation. In this context, there is an increasing need for more accurate evaluations of wind turbine power curves. A novel methodology to model the power curves of wind turbines, which combines the use of artificial neural networks (ANN) and Fuzzy logic rules, is proposed in this paper. This methodology assesses the role of environmental temperature in the power curve and the impact of temperature increases on wind energy production. The application of this methodology is illustrated with the simulation of the impact of global warming on the electricity generation of a wind farm. Due to the non-linear relationship between the power output of a turbine and its primary and derived parameters, it is shown that ANN combined with an expert system formed by a Fuzzy logic module fit power curve modeling processes well. The application of the methodology shows that an increase in temperatures would trigger a small reduction in the performance of wind turbines.

This paper investigates the use of deep learning techniques in order to perform energy demand forecasting. To this end, the authors propose a mixed architecture consisting of a convolutional neural network (CNN) coupled with an artificial neural network (ANN), with the main objective of taking advantage of the virtues of both structures: the regression capabilities of the artificial neural network and the feature extraction capacities of the convolutional neural network. The proposed structure was trained and then used in a real setting to provide a French energy demand forecast using Action de Recherche Petite Echelle Grande Echelle (ARPEGE) forecasting weather data. The results show that this approach outperforms the reference Réseau de Transport d’Electricité (RTE, French transmission system operator) subscription-based service. Additionally, the proposed solution obtains the highest performance score when compared with other alternatives, including Autoregressive Integrated Moving Average (ARIMA) and traditional ANN models. This opens up the possibility of achieving high-accuracy forecasting using widely accessible deep learning techniques through open-source machine learning platforms.

The paper addresses the issue of modelling the demand for electricity in residential buildings with the use of artificial neural networks (ANNs). Real data for six houses in Switzerland fitted with measurement meters was used in the research. Their original frequency of 1 Hz (one-second readings) was re-sampled to a frequency of 1/600 Hz, which corresponds to a period of ten minutes. Out-of-sample forecasts verified the ability of ANNs to disaggregate electricity usage for specific applications (electricity receivers). Four categories of electricity consumption were distinguished: (i) fridge, (ii) washing machine, (iii) personal computer, and (iv) freezer. Both standard ANNs with multilayer perceptron architecture and newer types of networks based on deep learning were used. The simulations included over 10,000 ANNs with different architecture (number of neurons and structure of their connections), type and number of input variables, formulas of activation functions, training algorithms, and other parameters. The research confirmed the possibility of using ANNs to model the disaggregation of electricity consumption based on low frequency data, and suggested ways to build highly optimised models.