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Dissertations / Theses on the topic 'Electricity load forecasting'

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Published: 4 June 2021

Last updated: 25 July 2025

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1

SOBRAL, ANA PAULA BARBOSA. "FORECASTING HOURLY ELECTRICITY LOAD FOR LIGHT." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7464@1.

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Nessa dissertação é desenvolvido um modelo de previsão de curto prazo para cargas horárias empregando informações climáticas. Tal modelo é montado para a companhia de eletricidade LIGHT. O modelo proposto combina diferentes metodologias, são elas: Redes Neurais, Métodos Estatísticos e Lógica Nebulosa. Primeiramente, emprega-se o Mapa Auto-Organizável de Kohonen para identificar as curvas típicas de carga que são incluídas em um modelo de previsão estatística. Com intuito de melhorar o desempenho do modelo em termos do erro de previsão é adicionado, através de Lógica Nebulosa, o efei

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Alam, Samiul. "Recurrent neural networks in electricity load forecasting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233254.

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In this thesis two main studies are conducted to compare the predictive capabilities of feed-forward neural networks (FFNN) and long short-term memory networks (LSTM) in electricity load forecasting. The first study compares univariate networks using past electricity load, as well as multivariate networks using past electricity load and air temperature, in day-ahead load forecasting using varying lookback periods and sparsity of past observations. The second study compares FFNNs and LSTMs of different complexities (i.e. network sizes) when restrictions imposed by limitations of the real world

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3

Kardehi, Moghaddam Mehdi. "Introducing system-based spatial electricity load forecasting." Thesis, Kardehi Moghaddam, Mehdi (2016) Introducing system-based spatial electricity load forecasting. PhD thesis, Murdoch University, 2016. https://researchrepository.murdoch.edu.au/id/eprint/33975/.

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The main motivation of this research is to help reduce the Green House Gases (GHG) emissions of the electricity sector, and counteract the effects on nature and people. Traditional methods of power planning are not optimised to achieve this, and only consider Capital Expenditure (Capex) and Operational Expenditure (Opex) reduction as their main objectives. Minimising GHG emissions is now an additional objective of power planning. One way of achieving this is by optimising the distance of generators to the loads to reduce the transmission losses, and also by harnessing the available regional so

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Shepero, Mahmoud. "Modeling and forecasting the load in the future electricity grid : Spatial electric vehicle load modeling and residential load forecasting." Licentiate thesis, Uppsala universitet, Fasta tillståndets fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-359432.

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The energy system is being transitioned to increase sustainability. This transition has been accelerated by the increased awareness about the adverse effects of the greenhouse gas (GHG) emissions into the atmosphere. The transition includes switching to electricity as the energy carrier in some sectors, e.g., transportation, increasing the contribution of renewable energy sources (RES) to the grid, and digitalizing the grid services. Electric vehicles (EVs) are promoted and subsidized in many countries among the sustainability initiatives. Consequently, the global sales of EVs rapidly increase

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Zhao, Lu. "Machine Learning in Electricity Load Forecasting of Prosumer Buildings." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298022.

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Short-term load forecasting plays a key role in energy optimizations such as peaking shaving and cost arbitrage. Forecasting the aggregated load of a city or region has been researched for years and produced accurate results with time lead ranging from an hour to a week. However, little attention has been paid to the building level due to the fact that its dynamics are considerably different from those of a utility or other middle or large-scale customers. This thesis work focuses on short-term load forecasting at a building level, which is more challenging and will be taken as the pre-work fo

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SILVA, HELIO FRANCISCO DA. "ON ADDRESSING IRREGULARITIES IN ELECTRICITY LOAD TIME-SERIES AND SHORT TERM LOAD FORECASTING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2001. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=1737@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>As alterações na legislação do Setor de Energia Elétrica Brasileiro em fins do milênio passado, provocou profundas mudanças no planejamento da Operação do Sistema e na Comercialização de energia elétrica no Brasil. O desmembramento das atividades de geração, de transmissão e de distribuição de energia elétrica criou novas características no comportamento dos Agentes Concessionários e as previsões de demanda por energia elétrica, que sempre foram ferramenta importante, por exemplo, na programação da operação, passaram a ser

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7

Berk, Kevin [Verfasser]. "Probabilistic Forecasting of Electricity Load for Industrial Enterprises / Kevin Berk." München : Verlag Dr. Hut, 2017. http://d-nb.info/1135594392/34.

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Al-Aoudah, Ahmed A. "Long term load forecasting for the Central Region of Saudi Arabia." Thesis, Cranfield University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250633.

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9

FARIAS, DOUGLAS ALEXANDER ALVES DE. "DAILY ELECTRICITY FORECASTING IN LOAD LEVELS, COMBINING STATISTICAL AND COMPUTATIONAL INTELLIGENCE TOOLS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13211@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>OPERADOR NACIONAL DO SISTEMA ELÉTRICO<br>Esta dissertação apresenta um estudo sobre o comportamento da carga de energia agregada em intervalos temporais dentro de um mesmo dia. Esse tipo de agregação já vem sendo utilizado no setor elétrico brasileiro, sob a forma de três patamares de carga, denominados leve, média e pesada. No entanto, tais patamares são sempre obtidos indiretamente, a partir da agregação da carga horária, não tendo sido encontrado, até a publicação dessa dissertação, nenhum tratamento de forma direta dos mesmos. O t

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10

Oscar, Nordström. "Multivariate Short-term Electricity Load Forecasting with Deep Learning and exogenous covariates." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-183982.

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Maintaining the electricity balance between supply and demand is a challenge for electricity suppliers. If there is an under or overproduction, it entails financial costs and affects consumers and the climate. To better understand how to maintain the balance, can the suppliers use short-term forecasts of electricity load. Hence it is of paramount importance that the forecasts are reliable and of high accuracy. Studies show that time series modeling moves towards more data-driven methods, such as Artificial Neural Networks due to their ability to extract complex relationships and flexibility. T

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van, der Meer Dennis. "Spatio-temporal probabilistic forecasting of solar power, electricity consumption and net load." Licentiate thesis, Uppsala universitet, Fasta tillståndets fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-363448.

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The increasing penetration of renewable energy sources into the electricity generating mix poses challenges to the operational performance of the power system. Similarly, the push for energy efficiency and demand response—i.e., when electricity consumers are encouraged to alter their demand depending by means of a price signal—introduces variability on the consumption side as well. Forecasting is generally viewed as a cost-efficient method to mitigate the adverse effects of the aforementioned energy transition because it enables a grid operator to reduce the operational risk by, e.g., unit-com

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12

Li, Zili. "Topics in deregulated electricity markets." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/98895/1/Zili_Li_Thesis.pdf.

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The deregulation and introduction of market oriented competition has been a feature of major electricity markets worldwide in recent decades. This deregulation has given rise to a myriad of new challenges that impinge upon the decisions of market regulators, market operators and electricity generators and electricity retailers. This thesis is an investigation of three challenges in the national electricity market of Australia that have emerged in the post deregulation era. These are, respectively, forecasting load, the strategic bidding and rebidding behaviour of generators, and the effect of

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13

Sherwood, P. M. "The application of short-term forecasting techniques applied to the control of electrical load in an energy management scheme." Thesis, University of Strathclyde, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382459.

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14

Paisios, Andreas. "Profiling and disaggregation of electricity demands measured in MV distribution networks." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28777.

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Despite the extensive deployment of smart-meters (SMs) at the low-voltage (LV) level, which are either fully operational or will be in the near future, distribution network operators (DNOs) are still relying on a limited number of permanently installed monitoring devices at primary and secondary medium-voltage (MV) substations, for purposes of network operation and control, as well as to inform and facilitate trading interactions between generators, distributors and suppliers. Accordingly, improved and sufficiently developed models for the analysis of aggregate demands at the MV-level are requ

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15

Dubois, Benjamin. "Apprentissage multitâche pour la prévision de la consommation électrique." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC1031.

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Nous étudions la prévision du jour pour le lendemain de la consommation électrique agrégée à la maille des points de livraison, à partir des informations calendaires, des prévisions météorologiques et des valeurs récentes de ces séries temporelles. Ce travail s’inscrit dans un domaine de recherche plus large, qui participe à la modernisation du système électrique français. Avec la pénétration des énergies renouvelables et l’apparition de nouveaux modes de consommation, le besoin d’améliorer la qualité des prévisions au niveau local devient de plus en plus pressant. Acteur central du système él

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16

Cai, Mengmeng. "A Profit-Neutral Double-price-signal Retail Electricity Market Solution for Incentivizing Price-responsive DERs Considering Network Constraints." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99094.

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Emerging technologies, including distributed energy resources (DERs), internet-of-things and advanced distribution management systems, are revolutionizing the power industry. They provide benefits like higher operation flexibility and lower bulk grid dependency, and are moving the modern power grid towards a decentralized, interconnected and intelligent direction. Consequently, the emphasis of the system operation management has been shifted from the supply-side to the demand-side. It calls for a reconsideration of the business model for future retail market operators. To address this need, th

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Li, Jiasen. "Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's Market." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663082026476.

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Rana, Md Mashud. "Energy time series prediction." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/11745.

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Reliable operations and economical utilization of power systems require electricity load forecasting at a wide range of forecasting horizons. The objective of this thesis is two-fold: developing accurate prediction models for electricity load forecasting, and quantifying the load forecasting uncertainty. At first, we consider the task of feature selection for electricity load forecasting. We propose a two-step approach - identifying a set of candidate features based on the data characteristics and then selecting a subset of them using four different methods. We evaluate the performance of th

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Wang, Yaoping. "Climate Change and Its Effects on the Energy-Water Nexus." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534307556870925.

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Thouvenot, Vincent. "Estimation et sélection pour les modèles additifs et application à la prévision de la consommation électrique." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLS184/document.

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L'électricité ne se stockant pas aisément, EDF a besoin d'outils de prévision de consommation et de production efficaces. Le développement de nouvelles méthodes automatiques de sélection et d'estimation de modèles de prévision est nécessaire. En effet, grâce au développement de nouvelles technologies, EDF peut étudier les mailles locales du réseau électrique, ce qui amène à un nombre important de séries chronologiques à étudier. De plus, avec les changements d'habitude de consommation et la crise économique, la consommation électrique en France évolue. Pour cette prévision, nous adoptons ici u

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Vilmarest, Joseph de. "Modèles espace-état pour la prévision de séries temporelles. Application aux marchés électriques." Thesis, Sorbonne université, 2022. http://www.theses.fr/2022SORUS108.

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L'électricité étant difficile à stocker, prévoir la demande est un enjeu majeur pour maintenir l'équilibre entre la production et la consommation. L'évolution des usages de l'électricité, le déploiement des énergies renouvelables, et plus récemment la crise du coronavirus, motivent l'étude de modèles qui évoluent au cours du temps, pour tenir compte des changements de comportements. L'objectif de ce travail est de proposer des méthodes adaptatives de prévision, et nous nous sommes intéressés tout spécialement au cadre des modèles espace-état. Dans ce paradigme, on représente l'environnement (o

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Keisler, Julie. "Automated Deep Learning : algorithms and software for energy sustainability." Electronic Thesis or Diss., Université de Lille (2022-....), 2025. http://www.theses.fr/2025ULILB001.

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Les technologies actuelles ne permettant le stockage que par des moyens coûteux et peu efficaces, l'électricité reste difficile à stocker à grande échelle. Pour le bon fonctionnement du réseau, il est ainsi important qu'à tout instant, l'électricité injectée dans le réseau soit égale à l'électricité consommée. Historiquement et encore aujourd'hui, pour maintenir cet équilibre, les moyens de production sont planifiés par anticipation de la demande; d'où l'importance de prévoir aussi précisément que possible la consommation électrique. Avec l'intégration massive des énergies renouvelables dont l

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Hsu, Chu, and 徐朮. "Short-term Electricity Load Forecasting." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/82643755611096521261.

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碩士<br>國立中山大學<br>應用數學系研究所<br>104<br>Short-term load forecasting is an essential instrument in power system planning, operation, and control. Many operations of power system are based on load forecasting, such as unit commitment problem, network security analysis etc. Overestimation of electricity load requirement will cause conservative operations, and result in many unnecessary transfers of electrical supplies or excessive waste of energy procurement, as well as costly large investments in electrical utilities. On the other hand, underestimation will cause the power generation not meeting the

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VEDANSHU. "MODELLING TECHNIQUES FOR ELECTRICITY LOAD FORECASTING." Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/17239.

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SinglelayerFeedforwardNeuralNetwork(FNN)isusedmanyatime asalastlayerinmodelssuchasseq2seqorasimpleRNNnetwork. The importance of such layer is to transform the output to our required dimensions. When it comes to weights and biases initialization, there is no such speciﬁc technique that could speed up the learning process. We could depend on deep network initialization techniques such as Xavier or He initialization. But such initialization fails to show much improvement in learning speed or accuracy. Zero Initialization (ZI) for weights of a single layer network is proposed here. We ﬁrst test th

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Chen, Tai-Ji, and 陳泰吉. "Rough Set Theory in Electricity Load Forecasting." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/43099887067325429949.

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碩士<br>國立暨南國際大學<br>資訊管理學系<br>95<br>In this paper, we built an electric power loading forecasting system to predict the percentages of electric consumptions in the future by using rough set theory (RST). How to dispatch electricity to appropriate place effectively to increase the efficiency of electric transportation and reduce reaction time of equipments will be an important object for workmen in electric factories. A superior forecasting system can provide not only precise results but a high quality environment of electric consumptions for compatriots. Feature selection is a valuable technique

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Jhang, Yu-Ren, and 張鈺仁. "The Peak Load Forecasting of Electricity in Taiwan." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/q64t74.

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LIN, Bing-Yi, and 林秉毅. "The study of electricity load forecasting accuracy improvements." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/j335zx.

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博士<br>中原大學<br>商學博士學位學程<br>106<br>With the rapid economic development, accurate forecasting of electricity load is necessary for many energy applications, such as energy generation, power system operation safety, load unit investment and energy marketing. A forecasting error in electricity load may increase operating cost. Therefore, overestimation of future load may result in oversupply, whereas underestimation of load will result in failure to provide adequate reserve capacity and imply high peaking cost. To generate enough electricity, it depends on every unit of a region to accurately forec

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Lin, Jing-Hui, and 林景徽. "Forecasting of Load and electricity Price in Deregulated Markets." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/58820075853748516638.

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碩士<br>國立雲林科技大學<br>電機工程系碩士班<br>92<br>In the deregulated electricity environment, power producers and consumers need accurate price forecasting to plan bidding strategies in order to maximum their benefits. Moreover, load effect electricity price greatly, so how to achieve load forecasting is also essential. This thesis presents multiple regression analysis and fuzzy system method to obtain the estimated load first, because the modeling of regression analysis is simple and useful, and the fuzzy system method use some experience rules can solve many problems. Sequentially, inputting the estimated

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"On addressing irregularities in electricity load time-series and short term load forecasting." Tese, MAXWELL, 2001. http://www.maxwell.lambda.ele.puc-rio.br/cgi-bin/db2www/PRG_0991.D2W/SHOW?Cont=1737:pt&Mat=&Sys=&Nr=&Fun=&CdLinPrg=pt.

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Madrid, Ernesto Javier Aguilar. "Short-Term Electricity Demand Forecasting with Machine Learning." Master's thesis, 2021. http://hdl.handle.net/10362/120626.

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Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics<br>An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydro-thermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need

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Cuambe, Isaura Denise Filipe. "Electricity load demand forecasting in Portugal using least-squares support vector machines." Master's thesis, 2013. http://hdl.handle.net/10400.1/3553.

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Dissertação de mest., Engenharia Informática, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2013<br>Electricity Load Demand (ELD) forecasting is a subject that is of interest mainly to producers and distributors and it has a great impact on the national economy. At the national scale it is not viable to store electricity and it is also difficult to estimate its consumption accurately enough in order to provide a better agreement between supply and demand and consequently less waste of energy. Thus, researchers from many areas have addressed this issue in a way to facilitate the ta

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Harsamizadeh, Tehrani Nima. "Demand-side participation & baseline load analysis in electricity markets." Thesis, 2016. http://hdl.handle.net/1828/7659.

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Demand participation is a basic ingredient of the next generation of power exchanges in electricity markets. A key challenge in implementing demand response stems from establishing reliable market frameworks so that purchasers can estimate the demand correctly, buy as economically as possible and have the means of hedging the risk of lack of supply. System operators also need ways of estimating responsive load behaviour to reliably operate the grid. In this context, two aspects of demand response are addressed in this study: scheduling and baseline estimation. The thesis presents a market cle

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Jiang, Dian-Sheng, and 江典聲. "Recurrent Neural Networks with Semi-parametric Model Bases for Short-Term Electricity Load Forecasting." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/w4z5s9.

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碩士<br>國立中山大學<br>應用數學系研究所<br>106<br>The short term electricity load forecasting is an important issue for power system management. In this work, the methodology of the Recurrent Neural Network (RNN) combining the semi-parametric regression modeling is investigated and evaluated for its applicability in short term load forecasting (STLF). Based on the data obtained from the Taiwan Power Company (TPC) from January 2015 to May 2018, we build the RNN model with input variables as those in the semi-parametric regression model, such as the periodic B-spline bases related to the intra-daily and the in

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Hsu, Che-Chiang, and 許哲強. "A study on the Construction and Application of Taiwan Regional Electricity Load Forecasting Analysis System." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/17649765361665682543.

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博士<br>國立成功大學<br>資源工程學系碩博士班<br>91<br>Electricity power is one of the major input factors in economic development. To continually support the economic to growth and meet power requirements in the future, load forecasting has become very important for electric utilities. Moreover, accurate load forecast can be helpful in developing power supply strategy，financing planning, market research ,and electricity management. Up to now, load forecasting emphasized on aggregate or sector load forecasting in Taiwan, but aggregate or sector load forecasting can not predict where the load takes place and not

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Farland, Jonathan. "Zonal And Regional Load Forecasting In The New England Wholesale Electricity Market: A Semiparametric Regression Approach." 2013. https://scholarworks.umass.edu/theses/1120.

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Power system planning, reliability analysis and economically efficient capacity scheduling all rely heavily on electricity demand forecasting models. In the context of a deregulated wholesale electricity market, using scheduling a region’s bulk electricity generation is inherently linked to future values of demand. Predictive models are used by municipalities and suppliers to bid into the day-ahead market and by utilities in order to arrange contractual interchanges among neighboring utilities. These numerical predictions are therefore pervasive in the energy industry. This research seeks to d

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Ke, Bow-Ren, and 柯博仁. "Study of Load Forecasting and Electricity Rate Structrue for Bulk Substations of Tamshui-Hsintien Line of Tapei Rapid Transit System." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/52412404968515099162.

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碩士<br>國立台灣工業技術學院<br>電機工程技術研究所<br>85<br>The importance of rapid transit systems in today's environment is fully recognized. They will play an increasingly more prominent role in the urbantransportation industry. The keystone of this thesis is to perform the simulation, calculation and forecast of the load of bulk substation of Tamshui-Hsintein line, and according to this result, to determine the rate structuresuitable for Taipei Rapid Transit System(TRTS). Energy Management Model(EMM) isused

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Lebotsa, Moshoko Emily. "Short term load forecasting using quantile regression with an application to the unit commitment problem." Diss., 2018.

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MSc (Statistics)<br>Department of Statistics<br>Generally, short term load forecasting is essential for any power generating utility. In this dissertation the main objective was to develop short term load forecasting models for the peak demand periods (i.e. from 18:00 to 20:00 hours) in South Africa using. Quantile semi-parametric additive models were proposed and used to forecast electricity demand during peak hours. In addition to this, forecasts obtained were then used to nd an optimal number of generating units to commit (switch on or o ) daily in order to produce the required elec

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Ravele, Thakhani. "Medium term load forecasting in South Africa using Generalized Additive models with tensor product interactions." Diss., 2018. http://hdl.handle.net/11602/1165.

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MSc (Statistics)<br>Department of Statistics<br>Forecasting of electricity peak demand levels is important for decision makers in Eskom. The overall objective of this study was to develop medium term load forecasting models which will help decision makers in Eskom for planning of the operations of the utility company. The frequency table of hourly daily demands was carried out and the results show that most peak loads occur at hours 19:00 and 20:00, over the period 2009 to 2013. The study used generalised additive models with and without tensor product interactions to forecast electrici

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Shin, Yoon Sung. "Three Essays on Energy Economics and Forecasting." Thesis, 2011. http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10689.

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This dissertation contains three independent essays relating energy economics. The first essay investigates price asymmetry of diesel in South Korea by using the error correction model. Analyzing weekly market prices in the pass-through of crude oil, this model shows asymmetric price response does not exist at the upstream market but at the downstream market. Since time-variant residuals are found by the specified models for both weekly and daily retail prices at the downstream level, these models are implemented by a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process. T

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Alani, Adeshina Yahaha. "Short-term multiple forecasting of electric energy loads with weather profiles for sustainable demand planning in smart grids for smart homes." Diss., 2018. http://hdl.handle.net/10500/25216.

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Abstract:

Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Therefore, effective prediction of future electricity consumption cannot be underestimated. Notably, repeated imbalance is noticed between the demand and supply of electricity, and this is affected by different weather profiles such as temperature, wind speed, dew point, humidity and pressure of the electricity consumption locati

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