Enhance Predbat: Input External Loads Via Automation

Introduction

This article delves into a proposal to enhance Predbat, a tool used for energy management, by improving the way external load data is fed into it. Currently, Predbat relies on forecasts for loads, solar generation, and tariff information to optimize its performance. However, there's a recognized gap in how external load data can be effectively integrated. This article will explore the proposed solution to allow enhancements to the current API and/or manual adjustment methods available.

Problem Statement: The Need for Improved External Load Input

Predbat's effectiveness hinges on accurate forecasts of various parameters, including energy loads. The existing methods for incorporating external load data have limitations, hindering the tool's ability to respond dynamically to real-world conditions. As discussed in this discussion, the current approaches lack the granularity and flexibility needed to manage diverse load types effectively. The heart of the issue lies in the inability to easily manage multiple, distinct load inputs with varying characteristics. For instance, consider the difference between a consistent heating load, a predictable domestic hot water (DHW) load, and a short-duration, high-intensity cooking load. Each of these load types behaves differently and requires specific handling.

Currently, users are constrained by the "all or nothing" nature of manual adjustments. They can set a single load delta or disable it entirely, with no intermediate control or the ability to manage multiple inputs independently. This lack of control becomes particularly problematic when dealing with loads that fluctuate based on external factors, such as heating demand driven by ambient temperature. In such cases, relying solely on historical data for forecasting proves inadequate. Therefore, there is a need for a more sophisticated method to integrate external load data into Predbat.

Proposed Solution: A Granular and Flexible Approach

The ideal solution would involve a more granular and flexible approach to managing external load data within Predbat. This could be achieved by introducing the following features:

Multiple, Labelled Inputs

The enhancement should allow users to define and manage multiple load inputs, each with a unique label. This would enable the separation of different load types, such as heating, DHW, and cooking, allowing for individual control and monitoring. By labeling each input, users can easily identify and adjust specific loads without affecting others. This is a significant improvement over the current system, where all external loads are treated as a single, undifferentiated entity.

Start and End Times (or Duration)

To accurately model load behavior, the system should allow users to specify the start and end times (or duration) of each load. This is particularly important for short-duration loads, such as cooking, which have a significant impact on energy demand but only for a limited time. By defining the duration of the load, Predbat can more accurately predict its impact on the overall energy profile.

Load Delta

The concept of "load delta" should be retained, allowing users to specify the change in energy consumption caused by the external load. This provides a simple and intuitive way to quantify the impact of the load on the overall energy balance. The load delta can be expressed in terms of power (e.g., kW) or energy (e.g., kWh), depending on the specific needs of the user.

Optional Load Profile

For more complex loads, the system should optionally support the definition of a load profile. This would allow users to specify how the load varies over time, such as a higher initial power draw followed by a lower steady-state consumption. A load profile could be represented as a series of data points defining the power consumption at different times during the load's duration. This would enable Predbat to model the load's behavior more accurately, leading to improved energy management decisions.

Alternatives Considered and Their Limitations

As highlighted in the linked discussion, alternative approaches have been explored, but they come with limitations. Using the existing API or manual selection methods offers some level of control, but they fall short of providing the desired granularity and flexibility. The manual selection method, in particular, suffers from the "all or nothing" limitation, making it difficult to manage multiple inputs with varying characteristics. While these methods can be used to set a single load delta or turn it off entirely, they lack the ability to fine-tune individual loads or manage multiple inputs independently.

Complementary Nature of the Proposed Solution

It's important to emphasize that the proposed solution isn't intended to replace existing methods, such as the built-in load forecasting capabilities of Predbat or the use of PredAI. These mechanisms are effective for forecasting loads based on historical data and are expected to cover most of the future load with sufficient accuracy for Predbat's operation. Instead, the proposed enhancement aims to complement these existing methods by allowing users to augment the load forecast with external data, especially in cases where historical data is less reliable or relevant. For example, heating demand, which is heavily influenced by external temperature, may not be accurately predicted based solely on historical patterns. In such scenarios, the ability to incorporate external temperature data and its impact on heating load becomes crucial for accurate energy management.

Use Cases and Benefits

Enhanced Accuracy

By incorporating external data sources like weather forecasts or real-time occupancy data, Predbat can achieve a more accurate representation of energy demand. This leads to better decision-making regarding energy storage and usage, ultimately optimizing energy efficiency and cost savings.

Improved Responsiveness

The proposed enhancement enables Predbat to respond more dynamically to changing conditions. For example, if a sudden cold snap is forecasted, the system can proactively adjust heating schedules to ensure optimal comfort while minimizing energy consumption. This responsiveness is crucial for adapting to unpredictable events and maximizing energy efficiency.

Better Integration with Smart Home Systems

The enhanced API would facilitate seamless integration with smart home systems. This allows for real-time data exchange and control, enabling Predbat to make informed decisions based on the current state of the home environment. For example, occupancy sensors can provide data on the number of people present in the house, which can be used to adjust heating and lighting schedules accordingly.

Conclusion

In conclusion, enhancing Predbat with a more sophisticated method for incorporating external load data is crucial for improving its accuracy, responsiveness, and overall effectiveness. The proposed solution, which involves multiple labelled inputs, start and end times, load deltas, and optional load profiles, offers a significant improvement over existing methods. By complementing the built-in load forecasting capabilities with external data sources, Predbat can make more informed decisions, optimize energy usage, and contribute to a more sustainable energy future. This enhancement empowers users with greater control and flexibility in managing their energy consumption, leading to significant cost savings and environmental benefits.

To learn more about energy management systems and best practices, visit Energy.gov. This website provides valuable information and resources for individuals and organizations looking to improve their energy efficiency and reduce their carbon footprint.