Peramalan Jangka Sangat Pendek Daya Listrik PLTS On Grid Rumah Tinggal Menggunakan Metode Recurrent Neural Network Long Short Term Memory (RNN-LSTM) Berdasarkan Data Meteorologi

Pembangkit Listrik Tenaga Surya yang terhubung dengan jaringan PLN atau on grid dalam rumah tinggal berfungsi sebagai cadangan energi atau bahkan menjadi energi utama listrik pada rumah tinggal. Produksi daya listrik PLTS ini dipengaruhi oleh data meteorologi. Permalan daya pembangkitan listrik PLTS on grid berguna untuk mengetahui daya listrik yang diproduksi.  Pada penelitian ini menggunakan metode peramalan Recurrent Neural Network Long Short Term Memory. Tujuan penelitian ini adalah untuk memanfaatkan data meteorologi dan model peramalan RNN-LSTM untuk memprediksi daya listrik dalam jangka sangat pendek. Hasil dari penelitian ini model peramalan pada data uji sudah cukup mengikuti pola daya listrik aktual dan menunjukan nilai akurasi peramalan MSE 0,0139 dan MAPE 31,87%. Dapat disimpulkan bahwa metode RNN-LSTM memiliki intrepetasi peramalan dengan predikat layak.  Kata Kunci: PLTS on grid, Peramalan, RNN-LSTM

Photovoltaic Powe Analysis And Prediction Using Machine Learning Methods

Master of ScienceDepartment of Electrical and Computer EngineeringMajor Professor Not ListedThe stochastic nature of Photovoltaic power directly affects the stability of the grid. PV power forecasting allows power stations to know beforehand how much PV power will be available, which ensures that the grid remains in stabilized condition. PV power from India is analyzed and predicted using machine learning method

Long-term forecasting for growth of electricity load based on customer sectors

The availability of electrical energy is an important issue. Along with the growth of the human population, electrical energy also increases. This study addresses problems in the operation of the electric power system. One of the problems that occur is the power imbalance due to scale growth between demand and generation. Alternative countermeasures that can be done are to prepare for the possibility that will occur in the future or what we are familiar with forecasting. Forecasting using the multiple linear regression method with this research variable assumes the household sector, business, industry, and public sectors, and is considered by the influence of population, gross regional domestic product, and District Minimum Wage. In forecasting, it is necessary to evaluate the accuracy using mean absolute percentage error (MAPE). MAPE evaluation results show a value of 0.142 % in the household sector, 0.085 % in the business sector, 1.983 % in the industrial sector, and 0.131 % in the total customer sector

A Technical Review on Reliability and Economic Assessment Framework of Hybrid Power System with Solar and Wind Based Distributed Generators

Recent years have witnessed an upsurge in the penetration of solar and wind power. This can be chiefly attributed to worldwide climate concern and inclination towards low carbon sources. Owing to their abundant availability, solar and wind sources are projected to play a key part in de-carbonization of power sector. However, the variability of these sources and high initial cost pose a major challenge in their deployment. Thus, reliability and economic assessment is imperative to hybrid power system(HPS) with solar and wind integration. This paper tenders a survey on different aspects involved in reliability and economic assessment of HPS. Various techniques employed in uncertainty modelling of climatological parameters like solar irradiance and wind velocity have been deliberated. A detailed discussion on reliability evaluation parameters as well as techniques along with their merits and demerits has been carried out. In order to impart a sense of extensiveness to review, a discussion on economic evaluation metrics has also been presented. Further, author’s critical comments on review along with suggestions for possible research avenues has also been presented. The review presented in this paper is envisioned to facilitate a comprehensive guide towards evaluation of solar and wind energy based HP

A Review on Application of Artificial Intelligence Techniques in Microgrids

A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources (RERs), sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This paper presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Short-Term photovoltaic power forecasting based on long short term memory neural network and attention mechanism

10.1109/ACCESS.2019.2923006IEEE Access778063-7807

Near real-time global solar radiation forecasting at multiple time-step horizons using the long short-term memory network

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy)

Implementação de um sistema de previsão de produção fotovoltaica e consumos de um Edifício Inovador

Nos dias de hoje, existe a consciencialização de que a energia fotovoltaica é aquela que apresenta uma maior sustentabilidade energética dos edifícios. O objetivo desta dissertação foi prever a energia fotovoltaica produzida num edifício inovador, tendo por base os dados da temperatura ambiente e da irradiância, de modo a comparar os valores previstos com os valores reais. Para a realização dos sistemas de previsão usamos o software Matlab R2019b e um modelo de redes neuronais do tipo NARX. Foi realizado um pré-processamento dos dados fornecidos com ajustamento do intervalo entre cada amostra (15 minutos) com divisão dos dados em 2 partes (treino e validação). No treino usamos os dados entre 2015 e 2017 e na validação os do ano de 2018. Quanto ao intervalo de medição a considerar para as 2 amostras foi entre as 5h e as 21h, e as redes foram testadas com 5, 8, 10, 12 e 15 neurónios na camada oculta e com conjuntos de treino de 15, 20, 25 e 30 dias de dados. Após definirmos o conjunto de treino e o número de neurónios a aplicar nos preditores da temperatura e da irradiância (variáveis de entrada), estimamos a produção fotovoltaica (variável de saída). As principais conclusões relativamente à produção fotovoltaica demonstraram que, os meses que apresentam valores mais elevados de produção (real e prevista) e com maiores diferenças ao nível da produção total, foram os meses do início do ano, onde os dias são mais curtos. Os que apresentaram valores mais baixos, embora com uma tendência de subida, foram os meses entre a primavera e o outono (dias maiores). Foi também durante estes meses que observamos uma menor diferença na produção fotovoltaica, entre a produção real e prevista, o que demonstra que nesta fase houve um melhor comportamento dos dados usados, e por sinal uma melhor performance que pode ser explicada pela presença de mais dias de sol e dias mais longos