Electromagnetic Conversion into Kinetic and Thermal Energies.

The conversion of electromagnetic energy into magnetohydrodynamic energy occurs when the electric conductivity changes from negligible to finite values. This process is relevant during the epoch of reheating in the early universe at the end of inflation and before the emergence of the radiation-dominated era. We find that the conversion into kinetic and thermal energies is primarily the result of electric energy dissipation, while magnetic energy only plays a secondary role in this process. This means that since electric energy dominates over magnetic energy during inflation and reheating, significant amounts of electric energy can be converted into magnetohydrodynamic energy when conductivity emerges before the relevant length scales become stable.

Bioelectrochemical Purification of Biomass Polymer Derived Furfural Wastewater and Its Electric Energy Recovery.

With the increasing environmental pollution caused by waste polymers, the conversion of polymer components in biomass into valuable products is of great significance for waste management and resource recovery. A two-stage microbial fuel cell (MFC) was used to treat furfural wastewater in this study. The maximum output voltage was 240-250 mV and the power generation time in an operation cycle was 286 h. The degradation efficiency of furfural reached 99-100% (furfural concentration at 300-3000 mg/L) and was slightly reduced to 91% at 7000 mg/L. In addition, the BOD/COD ratio of the furfural wastewater increased from 0.31 to 0.48 after MFC processing. The molecular analysis of the anodic bacterial isolates indicated that the phylogenetic bacterial mixture was dominated by five active anaerobic bacteria with a similarity percentage above 99% for each strain: Burkholderia (B. burdella), Clostridium sensu stricto (Cymbidaceae), Klebsiella (Klebsiella), Ethanoligenens (anaerobic genus), and Acidocella (anaerobic genus); the mixture exhibited good properties to carry out bioelectricity generation in the microbial fuel cell. This indicates that the MFC has effectively degraded furfural for pollutant removal and power generation and is a promising clean method to treat furfural pollution in industry wastewater.

Sustainability and affordability of Chinese-funded renewable energy project in sub-Saharan Africa: a hybridized solid oxide fuel cell, temperature sensors, and lithium-based solar system approach.

Renewable energy projects are at the crux of all Chinese-funded investment in sub-Saharan Africa, which accounts for some 56% of all Chinese-led investments globally. However, the prevailing problem is that about 568 million people were still without electricity access in 2019 across urban and rural areas in sub-Saharan Africa, which does not commensurate with the United Nations Sustainable Development Goal (SDG7) of ensuring affordable and clean energy for all. Previous studies have assessed and improved the efficiency of integrated power generation systems often combined on three levels, power plant, solar panel, and fuel cells, and integrated into national grids or off-grid systems for a sustainable supply of power. This study has included a lithium-ion storage system as a key component in a hybridized renewable energy generation system for the first time that has proven to be efficient and investment worthy. The study also examines the operational parameters of Chinese-funded power plant projects in sub-Saharan Africa and their effectiveness in achieving SDG-7. The novelty of this study is evident in the proposed integrated multi-level hybrid technology model of solid oxide fuel cells, temperature point sensors, and lithium batteries powered by a solar system and embedded in thermal power plants as an alternative electrical energy system for domestic and industrial use in sub-Saharan Africa. Performance analysis of the proposed power generation model indicates its complementary capacity of generating additional energy output with thermodynamics energy and exergy efficiencies of 88.2% and 67.0% respectively. The outcome of this study draws the attention of Chinese investors, governments in sub-Saharan African countries, and top industry players to the following: to consider refocusing their energy sector policy initiatives and strategies towards exploring the lithium resource base in Africa, optimizing energy generation cost, recouping optimal profit from their renewable energy technology investments, and making electricity supply clean, sustainable, and affordable for use in sub-Saharan Africa.

Tandem-Parallel Electrochemical Cells to Produce Hydrogen Peroxide at Reduced Energy Consumption.

Large-scale industrialization of oxygen electroreduction requires producing hydrogen peroxide (H2O2) at large yield rates (current density >1 A cm-2, Faradic efficiency >95%). Under such vigorous reaction conditions, however, serious electric energy consumption (EEC) has been caused. According to the formula (EEC=Y×1000×R×F2172×FE2), a linear relationship can be identified between H2O2 yield rates (Y) and EEC, and therefore, achieving high yield rates (Y) while reducing EEC is very challenging in common electrochemical systems. In this work, we have designed a tandem-parallel oxygen electroreduction system composed of two oxygen electroreduction units. The tandem unit can effectively improve the Faradaic efficiency (FE) while the parallel section reduces total internal resistance (R). Consequently, the overall system can achieve a high H2O2 yield rate (592 mg h-1) with the lowest EEC (2.41 kWh kg-1) ever reported to the best of our knowledge. Further, the tandem-parallel system has shown promising stability by working for more than 10 cycles or 24 h. Besides oxygen electroreduction, other applications have been also demonstrated for the tandem-parallel system that can generate H2O2 for in situ degradation of rhodamine B pollutant.

Excellent Dielectric Properties and Finite Element Simulation of Al-Epoxy Composites with Constructed Ultrathin Al2O3 Parallel-Plate Microcapacitors.

Metal-polymer dielectric composites show promising potential as embedded capacitors, whereas it remains a great challenge to achieve a high dielectric constant (εr) and low dielectric loss (tan δ) simultaneously. This work demonstrates a strategy for overcoming this challenge. Al nanoparticles with self-passivated ultrathin Al2O3 shells are compacted under the uniaxial pressure (P), and Al-epoxy composites are prepared by curing the liquid epoxy monomer that infiltrates into Al compacts. The contacting regions between adjacent Al nanoparticles are flattened and enlarged during the compacting process, so that the ultrathin Al2O3 parallel-plate microcapacitors are constructed by the insulating Al2O3 shells and conductive Al cores. The composite with P of 100 MPa and Al volume fraction (υAl) of 53.7% exhibits the εr of 189 at 10 kHz, which is much higher than the εr (48-102) of 0-3 type Al-polymer composites with similar υAl and even higher than the highest εr (160) reported in the Al-polymer composite with υAl > 80%. Furthermore, the present composites show low tan δ (<0.03) and good frequency and temperature stability of εr. The finite element simulation proves that the construction and enlargement of ultrathin Al2O3 parallel-plate microcapacitors dramatically increase the electric energy stored in Al2O3 and therefore greatly improve the εr.

Energy-efficient reuse of bio-treated textile wastewater by a porous-structure electrochemical PbO2 filter: Performance and mechanism.

In this work, a novel porous-structure electrochemical PbO2 filter (PEF-PbO2) was developed to achieve the reuse of bio-treated textile wastewater. The characterization of PEF-PbO2 confirmed that its coating has a variable pore size that increases with depth from the substrate, and the pores with a size of 5 µm account for the largest proportion. The study on the role of this unique structure illustrated that PEF-PbO2 possesses a larger electroactive area (4.09 times) than the conventional electrochemical PbO2 filter (EF-PbO2) and enhanced mass transfer (1.39 times) in flow mode. The investigation of operating parameters with a special discussion of electric energy consumption suggested that the optimal conditions were a current density of 3 mA cm-2, Na2SO4 concentration of 10 g L-1 and pH value of 3, which resulted in 99.07% and 53.3% removal of Rhodamine B and TOC, respectively, together with an MCETOC of 24.6%. A stable removal of 65.9% COD and 99.5% Rhodamine B with a low electric energy consumption of 5.19 kWh kg-1 COD under long-term reuse of bio-treated textile wastewater indicated that PEF-PbO2 was durable and energy-efficient in practical applications. Mechanism study by simulation calculation illustrated that the part of the pore of the PEF-PbO2's coating with small size (5 µm) plays an important role in this excellent performance which provides the advantage of rich ·OH concentration, short pollutant diffusion distance and high contact possibility.

Advanced Process Control for Clinker Rotary Kiln and Grate Cooler.

The cement industry includes energy-intensive processes, e.g., clinker rotary kilns and clinker grate coolers. Clinker is obtained through chemical and physical reactions in a rotary kiln from raw meal; these reactions also involve combustion processes. The grate cooler is located downstream of the clinker rotary kiln with the purpose of suitably cooling the clinker. The clinker is cooled through the action of multiple cold air fan units as it is transported within the grate cooler. The present work describes a project where Advanced Process Control techniques are applied to a clinker rotary kiln and a clinker grate cooler. Model Predictive Control was selected as the main control strategy. Linear models with delays are obtained through ad hoc plant experiments and suitably included in the controllers' formulation. A cooperation and coordination policy is introduced between the kiln and the cooler controllers. The main objectives of the controllers are to control the rotary kiln and grate cooler critical process variables while minimizing the fuel/coal specific consumption of the kiln and the electric energy consumption of the cold air fan units within the cooler. The overall control system was installed on the real plant, obtaining significant results in terms of service factor and control and energy-saving performances.

Degradation of Agro-Industrial Wastewater Model Compound by UV-A-Fenton Process: Batch vs. Continuous Mode.

The degradation of a model agro-industrial wastewater phenolic compound (caffeic acid, CA) by a UV-A-Fenton system was investigated in this work. Experiments were carried out in order to compare batch and continuous mode. Initially, batch experiments showed that UV-A-Fenton at pH 3.0 (pH of CA solution) achieved a higher generation of HO•, leading to high CA degradation (>99.5%). The influence of different operational conditions, such as H2O2 and Fe2+ concentrations, were evaluated. The results fit a pseudo first-order (PFO) kinetic model, and a high kinetic rate of CA removal was observed, with a [CA] = 5.5 × 10−4 mol/L, [H2O2] = 2.2 × 10−3 mol/L and [Fe2+] = 1.1 × 10−4 mol/L (kCA = 0.694 min−1), with an electric energy per order (EEO) of 7.23 kWh m−3 order−1. Under the same operational conditions, experiments in continuous mode were performed under different flow rates. The results showed that CA achieved a steady state with higher space-times (θ = 0.04) in comparison to dissolved organic carbon (DOC) removal (θ = 0−0.020). The results showed that by increasing the flow rate (F) from 1 to 4 mL min−1, the CA and DOC removal rate increased significantly (kCA = 0.468 min−1; kDOC = 0.00896 min−1). It is concluded that continuous modes are advantageous systems that can be adapted to wastewater treatment plants for the treatment of real agro-industrial wastewaters.

Energy system digitization in the era of AI: A three-layered approach toward carbon neutrality.

The transition toward carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation. The transition to a carbon-neutral electric grid poses significant challenges to conventional paradigms of modern grid planning and operation. Much of the challenge arises from the scale of the decision-making and the uncertainty associated with the energy supply and demand. Artificial intelligence (AI) could potentially have a transformative impact on accelerating the speed and scale of carbon-neutral transition, as many decision-making processes in the power grid can be cast as classic, though challenging, machine-learning tasks. We point out that to amplify AI's impact on carbon-neutral transition of the electric energy systems, the AI algorithms originally developed for other applications should be tailored in three layers of technology, markets, and policy.

Psychiatric Symptoms in Parkinson's Disease Patients before and One Year after Subthalamic Nucleus Deep Brain Stimulation Therapy: Role of Lead Positioning and Not of Total Electrical Energy Delivered.

Parkinson's disease (PD) patients may experience neuropsychiatric symptoms, including depression, anxiety, sleep disturbances, psychosis, as well as behavioral and cognitive symptoms during all the different stages of the illness. Deep Brain Stimulation (DBS) therapy has proven to be successful in controlling the motor symptoms of PD and its possible correlation with the occurrence or worsening of neuropsychiatric symptoms has been reported. We aimed to assess the neuropsychiatric symptoms of 14 PD patients before and after one year of Subthalamic Nucleus (STN)-DBS and to correlate the possible changes to the lead placement and to the total electrical energy delivered. We assessed PD motor symptoms, depression, anxiety, apathy, impulsivity, and suicidality using clinician- and/or self-administered rating scales and correlated the results to the lead position using the Medtronic SuretuneTM software and to the total electrical energy delivered (TEED). At the 12-month follow-up, the patients showed a significant improvement in PD symptoms on the UPDRS (Unified Parkinson's disease Rating Scale) (−38.5%; p < 0.001) and in anxiety on the Hamilton Anxiety Rating Scale (HAM-A) (−29%; p = 0.041), with the most significant reduction in the physiological anxiety subscore (−36.26%; p < 0.001). A mild worsening of impulsivity was detected on the Barratt Impulsiveness Scale (BIS-11) (+9%; p = 0.048), with the greatest increase in the attentional impulsiveness subscore (+13.60%; p = 0.050). No statistically significant differences were found for the other scales. No correlation was found between TEED and scales' scores, while the positioning of the stimulating electrodes in the different portions of the STN was shown to considerably influence the outcome, with more anterior and/or medial lead position negatively influencing psychiatric symptoms.

Novel Rubber Composites Based on Copper Particles, Multi-Wall Carbon Nanotubes and Their Hybrid for Stretchable Devices.

New technologies are constantly addressed in the scientific community for updating novel stretchable devices, such as flexible electronics, electronic packaging, and piezo-electric energy-harvesting devices. The device promoted in the present work was found to generate promising ~6V and durability of >0.4 million cycles. This stretchable device was based on rubber composites. These rubber composites were developed by solution mixing of room temperature silicone rubber (RTV-SR) and nanofiller, such as multi-wall carbon nanotube (MWCNT) and micron-sized copper particles and their hybrid. The hybrid composite consists of 50:50 of both fillers. The mechanical stretchability and compressive modulus of the composites were studied in detail. For example, the compressive modulus was 1.82 MPa (virgin) and increased at 3 per hundred parts of rubber (phr) to 3.75 MPa (MWCNT), 2.2 MPa (copper particles) and 2.75 MPa (hybrid). Similarly, the stretching ability for the composites used in fabricating devices was 148% (virgin) and changes at 3 phr to 144% (MWCNT), 230% (copper particles) and 199% (hybrid). Hence, the hybrid composite was found suitable with optimum stiffness and robust stretching ability to be useful for stretching electronic devices explored in this work. These improved properties were tested for a real-time stretchable device, such as a piezoelectric energy-harvesting device and their improved voltage output and durability were reported. In the end, a series of experiments conducted were summarized and a discussion on the best candidate with higher properties useful for prospective applications was reported.

High-Performance Breaking and Intelligent of Miniature Circuit Breakers.

The exploitation and utilization of clean energy such as wind and photovoltaic power plays an important role in the reduction in carbon emissions to achieve the goal of "emission peak and carbon neutral", but such a quantity of clean energy accessing the electric system will foster the transition of the electric power system structure. The intelligentization of power equipment will be an inevitable trend of development. High breaking performance, remote control and a digital detection platform of miniature circuit breaker, a protective equipment of a power distribution system, have also been inevitable requirements of the power Iot system. Based on the above, this paper studies three aspects: high-performance AC and DC general switching technology, remote control technology and operation status' digital monitoring. A new DC non-polar breaking technology is proposed, which improves the short circuit breaking ability. An experimental prototype using the above techniques was fabricated and passed the DC 1000 V/10 kA short-circuit breaking test. On the basis of the above, an intelligent circuit breaker is developed, which contains multiple functions: remote switching, real-time temperature detection, energy metering and fault warning. Moreover, a software for digital condition monitoring and remote control is developed. This work has certain theoretical and practical significance for the development of the power Internet of things.

Springback Reduction of Ultra-High-Strength Martensitic Steel Sheet by Electrically Single-Pulsed Current.

This paper investigates the reduction of springback by an electrically single-pulsed current for an ultra-high-strength martensitic steel sheet, MART1470 1.2t. In order to evaluate the springback reduction by the electric current, V-bending tests were performed with various parameter-sets (current density and pulse duration). The amount of springback reduction was then calculated from the measured bent-angle of tested specimens. Experimental results show the springback is reduced with the increase in the current density, the pulse duration, and the electric energy density. In order to clarify thermal and athermal portions in the effect of electric current on the springback reduction, two ratios of force and isothermal flow stress were calculated based on bending theory. From the comparison of the ratios, it is noted that the athermal portion mainly contributes to the force relaxation, so the springback amount decreases. The athermal portion significantly increases as the electric energy density increases. Microstructures and micro-Vickers hardness were observed to confirm the applicability of the single-pulsed current to forming processes in practice. The springback reduction can be achieved up to 37.5% without severe changes in material properties when the electric energy density increases up to 281.3 mJ/mm3. Achievable reduction is 85.4% for the electric energy density of 500 mJ/mm3, but properties remarkably change.

Challenges and Opportunities of Polymer Nanodielectrics for Capacitive Energy Storage.

With the modern development of power electrification, polymer nanocomposite dielectrics (or nanodielectrics) have attracted significant research attention. The idea is to combine the high dielectric constant of inorganic nanofillers and the high breakdown strength/low loss of a polymer matrix for higher energy density polymer film capacitors. Although impressively high energy density has been achieved at the laboratory scale, there is still a large gap from the eventual goal of polymer nanodielectric capacitors. In this review, we focus on essential material issues for two types of polymer nanodielectrics, polymer/conductive nanoparticle and polymer/ceramic nanoparticle composites. Various material design parameters, including dielectric constant, dielectric loss, breakdown strength, high temperature rating, and discharged energy density will be discussed from both fundamental science and high-voltage capacitor application points of view. The objective is to identify advantages and disadvantages of the polymer nanodielectric approach against other approaches utilizing neat dielectric polymers and ceramics. Given the state-of-the-art understanding, future research directions are outlined for the continued development of polymer nanodielectrics for electric energy storage applications.

Techno-economic study for a gasification plant processing residues of sewage sludge and solid recovered fuels.

The energetic valorisation of wastes through gasification is a promising solution with a better environmental impact in terms of pollutant emissions compared with incineration, landfilling, and heat and power generation from fossil fuels. However, techno-economic studies are imperative to define the viability of these technologies and to optimise heat and power consumptions and costs. This work intended to develop a techno-economic analysis for a small-scale gasification plant processing mixtures of solid recovered fuels and sewage sludge, assuming a capacity of 883 kg/h and two different sale scenarios: (A) production of electric energy, and (B) production of hydrogen. Gasification tests and mass and energy flow analyses were carried out for the economic assessment. The results showed that both scenarios presented viability for implementation. Although scenario A was more attractive in the short-term period due to the lower payback period (9 year) and higher internal rate of return (IRR, 7.5%), the other option was more favourable at the end of plant's life once the net present value was greater (1,801,700 €). Based on the results of a sensitivity analysis, a conclusion could be drawn that the economic indicators payback period and IRR were most influenced by capital expenditures applied in the plant.

Structure and indicators of electric energy consumption in dairy wastewater treatment plant.

The aim of the research was to determine the indicators of electricity consumption in every stage of the dairy sewage treatment process in relation to the sewage flow and the load of removed organics (BOD5, COD) and nutrients (TN, TP). The research was conducted in a dairy wastewater treatment plant (WWTP) consisting of mechanical treatment, averaging tank, dissolved air flotation (DAF) and biological treatment with sequence batch reactors (SBRs). Energy consumption was measured with the help of transducers. Indicators of unit electricity consumption were determined on the basis of 95 measurement series of energy consumption, sewage flow and removed load. The mean value of total unit energy consumption relating to the flow for the entire WWTP was 2.29 kWh·m-3, while for biological treatment 1.17 kWh·m-3 and 0.05 kWh·m-3 for DAF. The mean values of indicators relating to removed pollutants load for the entire WWTP were: 1.89 kWh·kgrem BOD5-1, 1.30 kWh·kgrem COD-1, 48.61 kWh·kgrem TN-1 and 160.01 kWh·kgrem TP-1. During biological treatment, energy consumption indicators were on average: 1.65 kWh·kgrem BOD5-1 and 1.19 kWh·kgrem COD-1, 52.90 kWh·kgrem TN-1 and 141.26 kWh·kgrem TP-1, while for DAF: 0.12 kWh·kgrem BOD5-1, 0.09 kWh·kgrem COD -1, 3.85 kWh·kgrem TN-1 and 16.17 kWh·kgrem TP-1. It was found that the biological treatment in SBRs was responsible for 54.1% of the total energy consumption of dairy WWTP. Aerobic sewage sludge treatment accounted for 17.0% of total consumption, mechanical treatment 17.1%, deodorization 2.6%, and other (social, lighting etc.) 6.9%, while DAF only 2.3%. The real-time electricity metering system enabled the optimisation of the electricity consumption in the WWTP, taking into account its consumption in unit processes and the removed pollutants load. The application of this system enabled to make corrections that reduced energy consumption while maintaining the required treatment efficiency.

Addressing the impact of COVID-19 lockdown on energy use in municipal buildings: A case study in Florianópolis, Brazil.

COVID-19 has spread quickly to several countries following the initial outbreak of the disease. As a consequence, several measures have been taken to mitigate the virus spread worldwide. In the city of Florianópolis, in southern Brazil, a strict lockdown was implemented on 16 March 2020. Although commercial activities were allowed to resume 21 April, a complete lockdown of municipal public buildings (e.g., administrative buildings and schools) lasted up to 5 August 2020. Reports in the literature emphasize the influence of occupant presence and actions on energy use in buildings. Therefore, the objective of this study was to assess the impact of the COVID-19 lockdown on the electric energy use of municipal buildings in Florianópolis. A large database with monthly electric energy use data was provided by the City Hall and analyzed. Firstly, the consumer units were grouped into three categories: systems, services and buildings. This revealed that buildings were directly affected by the lockdown measures, but systems and services were not. Therefore, an in-depth evaluation of health centers, administrative buildings, elementary schools and nursery schools was conducted and mean electric energy reductions of 11.1 %, 38.6 %, 50.3 %, and 50.4 %, respectively, were observed. Although it may initially seem unexpected, municipal health centers had a small electric energy use reduction, because they were not directly responsible for COVID-19 treatment, as patients were forwarded to specific facilities. Walkthroughs and energy audits were performed in an administrative building, an elementary school, and a nursery school, to gain a deeper understanding of the consumption trends. It was observed that municipal buildings present a basal energy use intensity even when the buildings are unoccupied. Energy audits verified that stand-by loads and vital loads, such as lighting for safety and computer servers, play a key role in this share of energy use.

Virtual Reality Applied in a Thermoelectric Power Plant Planning

Abstract The planning of a new thermal power plant is linked to the various decision elements and evaluation criteria. Factors such as the plant's geographic positioning, primary energy supply points, paths, and means of delivery of this primary energy should be analyzed. Similar studies are imposed when studying the change of a thermoelectric plant's primary energy source occurs. In Brazil, several plants are currently investigating the exchange of their primary fuel from oil to gas due to the decrees issued by ANEEL. This paper presents software, which uses virtual reality to assist in the various stages of the planning process and in the analyses that must be performed. This software was developed for the Hidrotermica Group and had as the primary target the Borborema Thermoelectric Power Plant.

Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building.

Electric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an individual household in a smart building. In practice, there are many profiles in each smart building, which leads to time-consuming and expensive system resources. Therefore, this study develops a robust framework for the Multiple Electric Energy Consumption forecasting (MEC) of a smart building using Transfer Learning and Long Short-Term Memory (TLL), the so-called MEC-TLL framework. In this framework, we first employ a k-means clustering algorithm to cluster the daily load demand of many profiles in the training set. In this phase, we also perform Silhouette analysis to specify the optimal number of clusters for the experimental datasets. Next, this study develops the MEC training algorithm, which utilizes a cluster-based strategy for transfer learning the Long Short-Term Memory models to reduce the computational time. Finally, extensive experiments are conducted to compare the computational time and different performance metrics for multiple electric energy consumption forecasting on two smart buildings in South Korea. The experimental results indicate that our proposed approach is capable of economical overheads while achieving superior performances. Therefore, the proposed approach can be applied effectively for intelligent energy management in smart buildings.

Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation.

For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods.