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In this project, we want to analyze energy consumption in large households over a period of time. This will help in the effective planning and operation of power systems, developing optimal forecasting tools for energy operators to maximize profit and also to provide maximum satisfaction to energy consumers. For this, data for electric energy consumed in a city was collected for a particular period of time. Data was trained and the proposed model was validated using long short-term memory (LSTM) Model techniques. The model has been tested with actual energy consumption data and verified if this yields satisfactory performance.
Why: Problem statement
In this project, we want to analyse the energy consumption in large households over a period of time. This will help in effective planning and operation of power systems, developing optimal forecasting tools for energy operators to maximize profit and also to provide maximum satisfaction to energy consumers. For this, data for electric energy consumed in a city was collected for a particular period of time. Data was trained and the proposed model was validated using long short-term memory (LSTM) techniques. The model has been tested with actual energy consumption data and verified if this yields satisfactory performance.
Due to increasing globalization and industrialization, energy consumption has been found to increase constantly. It has been found that buildings are responsible for the biggest proportion of energy consumption; for example in India and China, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy consumption in buildings, different policies have been proposed, from utilizing bioclimatic architecture to the use of predictive models within control approaches. Here we talk about using an artificial neural network approach for forecasting electric energy consumption.
How: Solution description
We adapt two deep neural network architectures to energy disaggregation: 1) long short-term memory (LSTM); and 2) a network that regresses the start time, end time and average power demand of each appliance called Multilinear regression Algorithm. We use five metrics like ID, plans, date, time and meter readings to test the performance of these algorithms on real aggregate power data from appliances. Tests are performed against a house not seen during training and against houses seen during training.
Models/ Algorithms proposed
We use long short-term memory and Multilinear regression algorithm for analysis of electric energy consumption of the top households with the highest number of samples on an hourly basis based on the previous usage pattern. The major features taken for analysis include ID, plans, date, time and meter readings in KiloWatts per Hour.
General Assumptions: We analyse that certain households consume more electric energy based on the usage of general appliances.
Technical Assumptions: We analyse if a particular appliance or group of appliances consume more energy compared to other appliances.
How is it different from competition
Residential load forecasting has been playing an increasingly important role in developed cities. Due to the variability of residents’ activities, individual residential loads are usually too volatile to forecast accurately. It is shown that the forecasting accuracy can be notably improved by including appliance measurements in the training data. For this, energy consumption from residential households was analyzed using long short-term memory (LSTM) and Multilinear regression algorithm analysis. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset. We are not considering energy consumption from suburban areas which also consume substantial amount of electricity. Also, energy consumption from individual households are not taken into account.
Who are your customers
Common people and Industries
Project Phases and Schedule
Phase 1: Data collection
Phase 2: Data cleaning
Phase 3: Algorithm development
Phase 4: prediction
Software: Anaconda tool
Language: Python 3
Model: LSTM Model
Algorithm: Multilinear regression algorithm
Libraries used : Numpy, Pandas, Matplotlib, LSTM library