Impact Study of Temperature on the Time Series Electricity Demand of Urban Nepal for Short-Term Load Forecasting

Rajbhandari, Yaju; Marahatta, Anup; Ghimire, Bishal; Shrestha, Ashish; Gachhadar, Anand; Thapa, Amit; Chapagain, Kamal; Korba, Petr

doi:10.3390/asi4030043

Cited by 16 publications

(4 citation statements)

References 29 publications

(67 reference statements)

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“…In more detail, the Auto-Regressive Integrated Moving Average (ARIMA) model is used to forecast short-term values in a stochastic manner. In the ARIMA model formula, the three main terms, p, d, and q, represent the autoregressive term, the number of differences required to make the time series stationary, and the moving average term, respectively [67,68]. Figure 7 illustrates the steps taken to determine the values for p, d, and q in order to identify the ideal ARIMA model [65].…”

Section: Load Forecasting Modelmentioning

confidence: 99%

Enhanced demand side management for solar‐based isolated microgrid system: Load prioritisation and energy optimisation

Rajbhandari,

Marahatta,

Shrestha

et al. 2023

IET Smart Grid

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A novel control mechanism is presented for rural microgrids, standing out in the current literature with its advanced approach to load prioritisation and energy allocation. The system's main goal is to maximise energy supply to essential loads while effectively managing available resources. Distinct from traditional methods, this mechanism dynamically classifies loads according to user‐defined priorities, adjustable based on the control system's computational power and complexity. A critical feature is the utilisation of the Particle Swarm Optimisation (PSO) algorithm to optimise demand side management (DSM). This innovative approach leverages day‐ahead load and generation forecasts to ensure optimal energy distribution across load levels, maintaining continuous power supply to high‐priority loads and reducing blackout risks due to generation and load fluctuations. Analyses under stochastic scenarios demonstrate the robustness of the control action, with percentile‐based day‐ahead forecasting allowing for adaptation to significant variations in renewable energy generation patterns. The implementation results are significant, maintaining 100% supply continuity to essential loads throughout the day, even with generation fluctuations up to ‐20%. This marks a considerable improvement in load satisfaction, increasing it from 83% to 96%. A significant advancement in microgrid control is contributed, providing an adaptive, user‐centric approach that enhances load management and energy distribution, and facilitates more resilient and efficient microgrid systems in the face of highly variable renewable energy sources (RESs).

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Section: Load Forecasting Modelmentioning

confidence: 99%

Enhanced demand side management for solar‐based isolated microgrid system: Load prioritisation and energy optimisation

Rajbhandari,

Marahatta,

Shrestha

et al. 2023

IET Smart Grid

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“…Several climatic factors, such as temperature and humidity, and temporal factors influence the electrical load demand profile of a certain area [50]. The share of domestic load is quite significant in the entire electrical load been consumed.…”

Section: Input Parameter Descriptionmentioning

confidence: 99%

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

et al. 2021

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Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.

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“…The study of neural networks for the use of protection of transmission systems is not a new concept and has been ongoing for decades [34]. The use of neural network models finds application in several fields of research in engineering [35,36]. Kadam et al [37] opined that machine learning offers a broad range of algorithms that can be adapted to help with fault detection.…”

Section: Neural Networkmentioning

confidence: 99%

Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

Okojie¹,

Idoko²,

Herbert³

et al. 2021

ASI

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Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use.

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Impact Study of Temperature on the Time Series Electricity Demand of Urban Nepal for Short-Term Load Forecasting

Cited by 16 publications

References 29 publications

Enhanced demand side management for solar‐based isolated microgrid system: Load prioritisation and energy optimisation

Enhanced demand side management for solar‐based isolated microgrid system: Load prioritisation and energy optimisation

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

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